ANCOVA in
Reading Comprehension (Reading Comprehension)
Geiser C. Challco geiser@alumni.usp.br
NOTE:
- Teste ANCOVA para determinar se houve diferenças significativas no
Reading Comprehension (medido usando pre- e pos-testes).
- ANCOVA test to determine whether there were significant differences
in Reading Comprehension (measured using pre- and post-tests).
Setting Initial Variables
dv = "score.compreensao"
dv.pos = "score.compreensao.pos"
dv.pre = "score.compreensao.pre"
fatores2 <- c("genero","zona.participante","zona.escola","score.compreensao.quintile")
lfatores2 <- as.list(fatores2)
names(lfatores2) <- fatores2
fatores1 <- c("grupo", fatores2)
lfatores1 <- as.list(fatores1)
names(lfatores1) <- fatores1
lfatores <- c(lfatores1)
color <- list()
color[["prepost"]] = c("#ffee65","#f28e2B")
color[["grupo"]] = c("#bcbd22","#008000")
color[["genero"]] = c("#FF007F","#4D4DFF")
color[["zona.escola"]] = c("#AA00FF","#00CCCC")
color[["zona.participante"]] = c("#AA00FF","#00CCCC")
level <- list()
level[["grupo"]] = c("Controle","Experimental")
level[["genero"]] = c("F","M")
level[["zona.escola"]] = c("Rural","Urbana")
level[["zona.participante"]] = c("Rural","Urbana")
# ..
ymin <- 0
ymax <- 0
ymin.ci <- 0
ymax.ci <- 0
color[["grupo:genero"]] = c(
"Controle:F"="#ff99cb", "Controle:M"="#b7b7ff",
"Experimental:F"="#FF007F", "Experimental:M"="#4D4DFF",
"Controle.F"="#ff99cb", "Controle.M"="#b7b7ff",
"Experimental.F"="#FF007F", "Experimental.M"="#4D4DFF"
)
color[["grupo:zona.escola"]] = c(
"Controle:Rural"="#b2efef","Controle:Urbana"="#e5b2ff",
"Experimental:Rural"="#00CCCC", "Experimental:Urbana"="#AA00FF",
"Controle.Rural"="#b2efef","Controle.Urbana"="#e5b2ff",
"Experimental.Rural"="#00CCCC", "Experimental.Urbana"="#AA00FF"
)
color[["grupo:zona.participante"]] = c(
"Controle:Rural"="#b2efef","Controle:Urbana"="#e5b2ff",
"Experimental:Rural"="#00CCCC", "Experimental:Urbana"="#AA00FF",
"Controle.Rural"="#b2efef","Controle.Urbana"="#e5b2ff",
"Experimental.Rural"="#00CCCC", "Experimental.Urbana"="#AA00FF"
)
for (coln in c(
"palavras.lidas","score.compreensao","tri.compreensao",
"score.vocab","tri.vocab",
"score.vocab.ensinado","tri.vocab.ensinado","score.vocab.nao.ensinado","tri.vocab.nao.ensinado",
"score.CLPP","tri.CLPP","score.CR","tri.CR",
"score.CI","tri.CI","score.TV","tri.TV","score.TF","tri.TF","score.TO","tri.TO")) {
color[[paste0(coln,".quintile")]] = c("#BF0040","#FF0000","#800080","#0000FF","#4000BF")
level[[paste0(coln,".quintile")]] = c("1st quintile","2nd quintile","3rd quintile","4th quintile","5th quintile")
color[[paste0("grupo:",coln,".quintile")]] = c(
"Experimental.1st quintile"="#BF0040", "Controle.1st quintile"="#d8668c",
"Experimental.2nd quintile"="#FF0000", "Controle.2nd quintile"="#ff7f7f",
"Experimental.3rd quintile"="#8fce00", "Controle.3rd quintile"="#ddf0b2",
"Experimental.4th quintile"="#0000FF", "Controle.4th quintile"="#b2b2ff",
"Experimental.5th quintile"="#4000BF", "Controle.5th quintile"="#b299e5",
"Experimental:1st quintile"="#BF0040", "Controle:1st quintile"="#d8668c",
"Experimental:2nd quintile"="#FF0000", "Controle:2nd quintile"="#ff7f7f",
"Experimental:3rd quintile"="#8fce00", "Controle:3rd quintile"="#ddf0b2",
"Experimental:4th quintile"="#0000FF", "Controle:4th quintile"="#b2b2ff",
"Experimental:5th quintile"="#4000BF", "Controle:5th quintile"="#b299e5")
}
gdat <- read_excel("../data/data.xlsx", sheet = "triagem.wg.wo.st")
dat <- gdat
dat$grupo <- factor(dat[["grupo"]], level[["grupo"]])
for (coln in c(names(lfatores))) {
dat[[coln]] <- factor(dat[[coln]], level[[coln]][level[[coln]] %in% unique(dat[[coln]])])
}
dat <- dat[which(!is.na(dat[[dv.pre]]) & !is.na(dat[[dv.pos]])),]
dat <- dat[,c("id",names(lfatores),dv.pre,dv.pos)]
dat.long <- rbind(dat, dat)
dat.long$time <- c(rep("pre", nrow(dat)), rep("pos", nrow(dat)))
dat.long$time <- factor(dat.long$time, c("pre","pos"))
dat.long[[dv]] <- c(dat[[dv.pre]], dat[[dv.pos]])
for (f in c("grupo", names(lfatores))) {
if (is.null(color[[f]]) && length(unique(dat[[f]])) > 0)
color[[f]] <- distinctColorPalette(length(unique(dat[[f]])))
}
for (f in c(fatores2)) {
if (is.null(color[[paste0("grupo:",f)]]) && length(unique(dat[[f]])) > 0)
color[[paste0("grupo:",f)]] <- distinctColorPalette(length(unique(dat[["grupo"]]))*length(unique(dat[[f]])))
}
ldat <- list()
laov <- list()
lpwc <- list()
lemms <- list()
Descriptive Statistics
of Initial Data
df <- get.descriptives(dat, c(dv.pre, dv.pos), c("grupo"),
include.global = T, symmetry.test = T, normality.test = F)
df <- plyr::rbind.fill(
df, do.call(plyr::rbind.fill, lapply(lfatores2, FUN = function(f) {
if (nrow(dat) > 0 && sum(!is.na(unique(dat[[f]]))) > 1)
get.descriptives(dat, c(dv.pre,dv.pos), c("grupo", f),
symmetry.test = T, normality.test = F)
}))
)
df <- df[,c(fatores1[fatores1 %in% colnames(df)],"variable",
colnames(df)[!colnames(df) %in% c(fatores1,"variable")])]
| Controle |
|
|
|
|
score.compreensao.pre |
153 |
5.098 |
5.0 |
0 |
10 |
2.533 |
0.205 |
0.405 |
4.00 |
YES |
0.419 |
-0.742 |
| Experimental |
|
|
|
|
score.compreensao.pre |
115 |
5.522 |
6.0 |
1 |
10 |
2.773 |
0.259 |
0.512 |
5.00 |
YES |
-0.008 |
-1.121 |
|
|
|
|
|
score.compreensao.pre |
268 |
5.280 |
5.0 |
0 |
10 |
2.642 |
0.161 |
0.318 |
4.00 |
YES |
0.228 |
-0.953 |
| Controle |
|
|
|
|
score.compreensao.pos |
153 |
4.837 |
4.0 |
0 |
10 |
2.728 |
0.221 |
0.436 |
4.00 |
YES |
0.430 |
-0.917 |
| Experimental |
|
|
|
|
score.compreensao.pos |
115 |
5.183 |
5.0 |
0 |
10 |
2.624 |
0.245 |
0.485 |
4.00 |
YES |
0.305 |
-0.954 |
|
|
|
|
|
score.compreensao.pos |
268 |
4.985 |
5.0 |
0 |
10 |
2.684 |
0.164 |
0.323 |
4.00 |
YES |
0.372 |
-0.927 |
| Controle |
F |
|
|
|
score.compreensao.pre |
79 |
5.291 |
5.0 |
0 |
10 |
2.646 |
0.298 |
0.593 |
4.00 |
YES |
0.320 |
-0.902 |
| Controle |
M |
|
|
|
score.compreensao.pre |
74 |
4.892 |
4.0 |
1 |
10 |
2.408 |
0.280 |
0.558 |
3.00 |
NO |
0.503 |
-0.613 |
| Experimental |
F |
|
|
|
score.compreensao.pre |
55 |
6.345 |
6.0 |
1 |
10 |
2.654 |
0.358 |
0.718 |
3.00 |
YES |
-0.245 |
-0.919 |
| Experimental |
M |
|
|
|
score.compreensao.pre |
60 |
4.767 |
4.0 |
1 |
10 |
2.683 |
0.346 |
0.693 |
5.00 |
YES |
0.214 |
-1.189 |
| Controle |
F |
|
|
|
score.compreensao.pos |
79 |
5.253 |
5.0 |
1 |
10 |
2.574 |
0.290 |
0.577 |
4.00 |
YES |
0.359 |
-0.866 |
| Controle |
M |
|
|
|
score.compreensao.pos |
74 |
4.392 |
4.0 |
0 |
10 |
2.832 |
0.329 |
0.656 |
5.00 |
NO |
0.586 |
-0.912 |
| Experimental |
F |
|
|
|
score.compreensao.pos |
55 |
5.964 |
6.0 |
1 |
10 |
2.742 |
0.370 |
0.741 |
4.50 |
YES |
-0.079 |
-1.137 |
| Experimental |
M |
|
|
|
score.compreensao.pos |
60 |
4.467 |
4.0 |
0 |
10 |
2.310 |
0.298 |
0.597 |
3.00 |
NO |
0.601 |
-0.406 |
| Controle |
|
Rural |
|
|
score.compreensao.pre |
51 |
4.608 |
4.0 |
1 |
10 |
2.359 |
0.330 |
0.663 |
3.00 |
NO |
0.813 |
-0.092 |
| Controle |
|
Urbana |
|
|
score.compreensao.pre |
67 |
5.194 |
5.0 |
1 |
10 |
2.439 |
0.298 |
0.595 |
4.00 |
YES |
0.321 |
-0.815 |
| Controle |
|
|
|
|
score.compreensao.pre |
35 |
5.629 |
6.0 |
0 |
10 |
2.881 |
0.487 |
0.990 |
4.50 |
YES |
0.040 |
-1.161 |
| Experimental |
|
Rural |
|
|
score.compreensao.pre |
46 |
5.457 |
5.5 |
1 |
10 |
2.730 |
0.403 |
0.811 |
4.50 |
YES |
-0.037 |
-1.146 |
| Experimental |
|
Urbana |
|
|
score.compreensao.pre |
43 |
5.256 |
6.0 |
1 |
10 |
2.821 |
0.430 |
0.868 |
5.50 |
YES |
-0.069 |
-1.251 |
| Experimental |
|
|
|
|
score.compreensao.pre |
26 |
6.077 |
6.0 |
2 |
10 |
2.799 |
0.549 |
1.131 |
5.00 |
YES |
0.145 |
-1.431 |
| Controle |
|
Rural |
|
|
score.compreensao.pos |
51 |
4.353 |
4.0 |
1 |
10 |
2.644 |
0.370 |
0.744 |
3.00 |
NO |
0.890 |
-0.151 |
| Controle |
|
Urbana |
|
|
score.compreensao.pos |
67 |
5.194 |
5.0 |
1 |
10 |
2.693 |
0.329 |
0.657 |
4.00 |
YES |
0.205 |
-1.062 |
| Controle |
|
|
|
|
score.compreensao.pos |
35 |
4.857 |
4.0 |
0 |
10 |
2.881 |
0.487 |
0.990 |
5.50 |
YES |
0.246 |
-1.311 |
| Experimental |
|
Rural |
|
|
score.compreensao.pos |
46 |
5.217 |
5.0 |
0 |
10 |
2.724 |
0.402 |
0.809 |
4.75 |
YES |
0.163 |
-1.146 |
| Experimental |
|
Urbana |
|
|
score.compreensao.pos |
43 |
5.326 |
5.0 |
1 |
10 |
2.598 |
0.396 |
0.799 |
4.00 |
YES |
0.294 |
-0.966 |
| Experimental |
|
|
|
|
score.compreensao.pos |
26 |
4.885 |
5.0 |
1 |
10 |
2.566 |
0.503 |
1.037 |
3.00 |
NO |
0.574 |
-0.773 |
| Controle |
|
|
Rural |
|
score.compreensao.pre |
52 |
5.654 |
5.0 |
1 |
10 |
2.351 |
0.326 |
0.654 |
4.00 |
YES |
0.392 |
-0.947 |
| Controle |
|
|
Urbana |
|
score.compreensao.pre |
101 |
4.812 |
4.0 |
0 |
10 |
2.587 |
0.257 |
0.511 |
4.00 |
NO |
0.500 |
-0.674 |
| Experimental |
|
|
Rural |
|
score.compreensao.pre |
35 |
6.229 |
6.0 |
1 |
10 |
2.734 |
0.462 |
0.939 |
4.00 |
YES |
-0.158 |
-1.011 |
| Experimental |
|
|
Urbana |
|
score.compreensao.pre |
80 |
5.213 |
5.0 |
1 |
10 |
2.750 |
0.307 |
0.612 |
4.50 |
YES |
0.059 |
-1.204 |
| Controle |
|
|
Rural |
|
score.compreensao.pos |
52 |
5.173 |
4.5 |
1 |
10 |
2.728 |
0.378 |
0.759 |
4.25 |
YES |
0.362 |
-1.128 |
| Controle |
|
|
Urbana |
|
score.compreensao.pos |
101 |
4.663 |
4.0 |
0 |
10 |
2.725 |
0.271 |
0.538 |
5.00 |
YES |
0.467 |
-0.844 |
| Experimental |
|
|
Rural |
|
score.compreensao.pos |
35 |
5.486 |
5.0 |
1 |
10 |
2.853 |
0.482 |
0.980 |
4.50 |
YES |
0.147 |
-1.269 |
| Experimental |
|
|
Urbana |
|
score.compreensao.pos |
80 |
5.050 |
5.0 |
0 |
10 |
2.525 |
0.282 |
0.562 |
4.00 |
YES |
0.359 |
-0.836 |
| Controle |
|
|
|
1st quintile |
score.compreensao.pre |
6 |
0.833 |
1.0 |
0 |
1 |
0.408 |
0.167 |
0.428 |
0.00 |
few data |
0.000 |
0.000 |
| Controle |
|
|
|
2nd quintile |
score.compreensao.pre |
42 |
2.595 |
3.0 |
2 |
3 |
0.497 |
0.077 |
0.155 |
1.00 |
few data |
0.000 |
0.000 |
| Controle |
|
|
|
3rd quintile |
score.compreensao.pre |
61 |
4.852 |
5.0 |
4 |
6 |
0.813 |
0.104 |
0.208 |
2.00 |
few data |
0.000 |
0.000 |
| Controle |
|
|
|
4th quintile |
score.compreensao.pre |
26 |
7.462 |
7.0 |
7 |
8 |
0.508 |
0.100 |
0.205 |
1.00 |
few data |
0.000 |
0.000 |
| Controle |
|
|
|
5th quintile |
score.compreensao.pre |
18 |
9.778 |
10.0 |
9 |
10 |
0.428 |
0.101 |
0.213 |
0.00 |
few data |
0.000 |
0.000 |
| Experimental |
|
|
|
1st quintile |
score.compreensao.pre |
9 |
1.000 |
1.0 |
1 |
1 |
0.000 |
0.000 |
0.000 |
0.00 |
few data |
0.000 |
0.000 |
| Experimental |
|
|
|
2nd quintile |
score.compreensao.pre |
21 |
2.286 |
2.0 |
2 |
3 |
0.463 |
0.101 |
0.211 |
1.00 |
few data |
0.000 |
0.000 |
| Experimental |
|
|
|
3rd quintile |
score.compreensao.pre |
41 |
5.049 |
5.0 |
4 |
6 |
0.835 |
0.130 |
0.264 |
2.00 |
few data |
0.000 |
0.000 |
| Experimental |
|
|
|
4th quintile |
score.compreensao.pre |
25 |
7.520 |
8.0 |
7 |
8 |
0.510 |
0.102 |
0.210 |
1.00 |
few data |
0.000 |
0.000 |
| Experimental |
|
|
|
5th quintile |
score.compreensao.pre |
19 |
9.632 |
10.0 |
9 |
10 |
0.496 |
0.114 |
0.239 |
1.00 |
few data |
0.000 |
0.000 |
| Controle |
|
|
|
1st quintile |
score.compreensao.pos |
6 |
2.500 |
2.5 |
1 |
4 |
1.049 |
0.428 |
1.101 |
1.00 |
YES |
0.000 |
-1.571 |
| Controle |
|
|
|
2nd quintile |
score.compreensao.pos |
42 |
3.048 |
3.0 |
0 |
6 |
1.592 |
0.246 |
0.496 |
2.00 |
YES |
0.207 |
-0.865 |
| Controle |
|
|
|
3rd quintile |
score.compreensao.pos |
61 |
4.279 |
4.0 |
1 |
10 |
2.259 |
0.289 |
0.579 |
3.00 |
NO |
0.690 |
-0.222 |
| Controle |
|
|
|
4th quintile |
score.compreensao.pos |
26 |
6.808 |
7.0 |
2 |
10 |
2.000 |
0.392 |
0.808 |
2.00 |
NO |
-0.553 |
-0.545 |
| Controle |
|
|
|
5th quintile |
score.compreensao.pos |
18 |
8.833 |
10.0 |
5 |
10 |
1.543 |
0.364 |
0.768 |
2.00 |
NO |
-0.917 |
-0.359 |
| Experimental |
|
|
|
1st quintile |
score.compreensao.pos |
9 |
3.556 |
3.0 |
2 |
6 |
1.667 |
0.556 |
1.281 |
3.00 |
YES |
0.490 |
-1.652 |
| Experimental |
|
|
|
2nd quintile |
score.compreensao.pos |
21 |
2.952 |
3.0 |
0 |
7 |
1.717 |
0.375 |
0.782 |
2.00 |
NO |
0.522 |
-0.256 |
| Experimental |
|
|
|
3rd quintile |
score.compreensao.pos |
41 |
4.341 |
4.0 |
1 |
8 |
1.741 |
0.272 |
0.549 |
2.00 |
YES |
0.260 |
-0.738 |
| Experimental |
|
|
|
4th quintile |
score.compreensao.pos |
25 |
6.360 |
7.0 |
2 |
10 |
2.215 |
0.443 |
0.914 |
3.00 |
YES |
-0.086 |
-1.022 |
| Experimental |
|
|
|
5th quintile |
score.compreensao.pos |
19 |
8.684 |
9.0 |
5 |
10 |
1.493 |
0.342 |
0.719 |
2.00 |
NO |
-1.003 |
-0.098 |
ANCOVA and Pairwise
for one factor: grupo
Without remove non-normal
data
pdat = remove_group_data(dat[!is.na(dat[["grupo"]]),], "score.compreensao.pos", "grupo")
pdat.long <- rbind(pdat[,c("id","grupo")], pdat[,c("id","grupo")])
pdat.long[["time"]] <- c(rep("pre", nrow(pdat)), rep("pos", nrow(pdat)))
pdat.long[["time"]] <- factor(pdat.long[["time"]], c("pre","pos"))
pdat.long[["score.compreensao"]] <- c(pdat[["score.compreensao.pre"]], pdat[["score.compreensao.pos"]])
aov = anova_test(pdat, score.compreensao.pos ~ score.compreensao.pre + grupo)
laov[["grupo"]] <- get_anova_table(aov)
pwc <- emmeans_test(pdat, score.compreensao.pos ~ grupo, covariate = score.compreensao.pre,
p.adjust.method = "bonferroni")
pwc.long <- emmeans_test(dplyr::group_by_at(pdat.long, "grupo"),
score.compreensao ~ time,
p.adjust.method = "bonferroni")
lpwc[["grupo"]] <- plyr::rbind.fill(pwc, pwc.long)
ds <- get.descriptives(pdat, "score.compreensao.pos", "grupo", covar = "score.compreensao.pre")
ds <- merge(ds[ds$variable != "score.compreensao.pre",],
ds[ds$variable == "score.compreensao.pre", !colnames(ds) %in% c("variable")],
by = "grupo", all.x = T, suffixes = c("", ".score.compreensao.pre"))
ds <- merge(get_emmeans(pwc), ds, by = "grupo", suffixes = c(".emms", ""))
ds <- ds[,c("grupo","n","mean.score.compreensao.pre","se.score.compreensao.pre","mean","se",
"emmean","se.emms","conf.low","conf.high")]
colnames(ds) <- c("grupo", "N", paste0(c("M","SE")," (pre)"),
paste0(c("M","SE"), " (unadj)"),
paste0(c("M", "SE"), " (adj)"), "conf.low", "conf.high")
lemms[["grupo"]] <- ds
Computing
ANCOVA and PairWise After removing non-normal data (OK)
wdat = pdat
res = residuals(lm(score.compreensao.pos ~ score.compreensao.pre + grupo, data = wdat))
non.normal = getNonNormal(res, wdat$id, plimit = 0.05)
wdat = wdat[!wdat$id %in% non.normal,]
wdat.long <- rbind(wdat[,c("id","grupo")], wdat[,c("id","grupo")])
wdat.long[["time"]] <- c(rep("pre", nrow(wdat)), rep("pos", nrow(wdat)))
wdat.long[["time"]] <- factor(wdat.long[["time"]], c("pre","pos"))
wdat.long[["score.compreensao"]] <- c(wdat[["score.compreensao.pre"]], wdat[["score.compreensao.pos"]])
ldat[["grupo"]] = wdat
(non.normal)
## NULL
aov = anova_test(wdat, score.compreensao.pos ~ score.compreensao.pre + grupo)
laov[["grupo"]] <- merge(get_anova_table(aov), laov[["grupo"]],
by="Effect", suffixes = c("","'"))
(df = get_anova_table(aov))
## ANOVA Table (type II tests)
##
## Effect DFn DFd F p p<.05 ges
## 1 score.compreensao.pre 1 265 288.858 2.57e-44 * 5.22e-01
## 2 grupo 1 265 0.023 8.80e-01 8.61e-05
| score.compreensao.pre |
1 |
265 |
288.858 |
0.00 |
* |
0.522 |
| grupo |
1 |
265 |
0.023 |
0.88 |
|
0.000 |
pwc <- emmeans_test(wdat, score.compreensao.pos ~ grupo, covariate = score.compreensao.pre,
p.adjust.method = "bonferroni")
| score.compreensao.pre*grupo |
score.compreensao.pos |
Controle |
Experimental |
265 |
-0.151 |
0.88 |
0.88 |
ns |
pwc.long <- emmeans_test(dplyr::group_by_at(wdat.long, "grupo"),
score.compreensao ~ time,
p.adjust.method = "bonferroni")
lpwc[["grupo"]] <- merge(plyr::rbind.fill(pwc, pwc.long), lpwc[["grupo"]],
by=c("grupo","term",".y.","group1","group2"),
suffixes = c("","'"))
| Controle |
time |
score.compreensao |
pre |
pos |
532 |
0.859 |
0.391 |
0.391 |
ns |
| Experimental |
time |
score.compreensao |
pre |
pos |
532 |
0.966 |
0.334 |
0.334 |
ns |
ds <- get.descriptives(wdat, "score.compreensao.pos", "grupo", covar = "score.compreensao.pre")
ds <- merge(ds[ds$variable != "score.compreensao.pre",],
ds[ds$variable == "score.compreensao.pre", !colnames(ds) %in% c("variable")],
by = "grupo", all.x = T, suffixes = c("", ".score.compreensao.pre"))
ds <- merge(get_emmeans(pwc), ds, by = "grupo", suffixes = c(".emms", ""))
ds <- ds[,c("grupo","n","mean.score.compreensao.pre","se.score.compreensao.pre","mean","se",
"emmean","se.emms","conf.low","conf.high")]
colnames(ds) <- c("grupo", "N", paste0(c("M","SE")," (pre)"),
paste0(c("M","SE"), " (unadj)"),
paste0(c("M", "SE"), " (adj)"), "conf.low", "conf.high")
lemms[["grupo"]] <- merge(ds, lemms[["grupo"]], by=c("grupo"), suffixes = c("","'"))
| Controle |
153 |
5.098 |
0.205 |
4.837 |
0.221 |
4.970 |
0.151 |
4.674 |
5.267 |
| Experimental |
115 |
5.522 |
0.259 |
5.183 |
0.245 |
5.005 |
0.174 |
4.663 |
5.347 |
Plots for ancova
plots <- oneWayAncovaPlots(
wdat, "score.compreensao.pos", "grupo", aov, list("grupo"=pwc), addParam = c("mean_ci"),
font.label.size=10, step.increase=0.05, p.label="p.adj",
subtitle = which(aov$Effect == "grupo"))
if (!is.null(nrow(plots[["grupo"]]$data)))
plots[["grupo"]] + ggplot2::ylab("Reading Comprehension") +
if (ymin.ci < ymax.ci) ggplot2::ylim(ymin.ci, ymax.ci)

plots <- oneWayAncovaBoxPlots(
wdat, "score.compreensao.pos", "grupo", aov, pwc, covar = "score.compreensao.pre",
theme = "classic", color = color[["grupo"]],
subtitle = which(aov$Effect == "grupo"))
if (length(unique(wdat[["grupo"]])) > 1)
plots[["grupo"]] + ggplot2::ylab("Reading Comprehension") +
ggplot2::scale_x_discrete(labels=c('pre', 'pos')) +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)

if (length(unique(wdat.long[["grupo"]])) > 1)
plots <- oneWayAncovaBoxPlots(
wdat.long, "score.compreensao", "grupo", aov, pwc.long,
pre.post = "time", theme = "classic", color = color$prepost)
if (length(unique(wdat.long[["grupo"]])) > 1)
plots[["grupo"]] + ggplot2::ylab("Reading Comprehension") +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)

Checking linearity
assumption
ggscatter(wdat, x = "score.compreensao.pre", y = "score.compreensao.pos", size = 0.5,
color = "grupo", add = "reg.line")+
stat_regline_equation(
aes(label = paste(..eq.label.., ..rr.label.., sep = "~~~~"), color = grupo)
) +
ggplot2::labs(subtitle = rstatix::get_test_label(aov, detailed = T, row = which(aov$Effect == "grupo"))) +
ggplot2::scale_color_manual(values = color[["grupo"]]) +
ggplot2::ylab("Reading Comprehension") +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)

Checking normality and
homogeneity
res <- augment(lm(score.compreensao.pos ~ score.compreensao.pre + grupo, data = wdat))
## # A tibble: 1 × 3
## variable statistic p.value
## <chr> <dbl> <dbl>
## 1 res$.resid 0.993 0.199
levene_test(res, .resid ~ grupo)
## # A tibble: 1 × 4
## df1 df2 statistic p
## <int> <int> <dbl> <dbl>
## 1 1 266 0.0795 0.778
ANCOVA and
Pairwise for two factors grupo:genero
Without remove non-normal
data
pdat = remove_group_data(dat[!is.na(dat[["grupo"]]) & !is.na(dat[["genero"]]),],
"score.compreensao.pos", c("grupo","genero"))
pdat = pdat[pdat[["genero"]] %in% do.call(
intersect, lapply(unique(pdat[["grupo"]]), FUN = function(x) {
unique(pdat[["genero"]][which(pdat[["grupo"]] == x)])
})),]
pdat[["grupo"]] = factor(pdat[["grupo"]], level[["grupo"]])
pdat[["genero"]] = factor(
pdat[["genero"]],
level[["genero"]][level[["genero"]] %in% unique(pdat[["genero"]])])
pdat.long <- rbind(pdat[,c("id","grupo","genero")], pdat[,c("id","grupo","genero")])
pdat.long[["time"]] <- c(rep("pre", nrow(pdat)), rep("pos", nrow(pdat)))
pdat.long[["time"]] <- factor(pdat.long[["time"]], c("pre","pos"))
pdat.long[["score.compreensao"]] <- c(pdat[["score.compreensao.pre"]], pdat[["score.compreensao.pos"]])
if (length(unique(pdat[["genero"]])) >= 2) {
aov = anova_test(pdat, score.compreensao.pos ~ score.compreensao.pre + grupo*genero)
laov[["grupo:genero"]] <- get_anova_table(aov)
}
if (length(unique(pdat[["genero"]])) >= 2) {
pwcs <- list()
pwcs[["genero"]] <- emmeans_test(
group_by(pdat, grupo), score.compreensao.pos ~ genero,
covariate = score.compreensao.pre, p.adjust.method = "bonferroni")
pwcs[["grupo"]] <- emmeans_test(
group_by(pdat, genero), score.compreensao.pos ~ grupo,
covariate = score.compreensao.pre, p.adjust.method = "bonferroni")
pwc <- plyr::rbind.fill(pwcs[["grupo"]], pwcs[["genero"]])
pwc <- pwc[,c("grupo","genero", colnames(pwc)[!colnames(pwc) %in% c("grupo","genero")])]
}
if (length(unique(pdat[["genero"]])) >= 2) {
pwc.long <- emmeans_test(dplyr::group_by_at(pdat.long, c("grupo","genero")),
score.compreensao ~ time,
p.adjust.method = "bonferroni")
lpwc[["grupo:genero"]] <- plyr::rbind.fill(pwc, pwc.long)
}
if (length(unique(pdat[["genero"]])) >= 2) {
ds <- get.descriptives(pdat, "score.compreensao.pos", c("grupo","genero"), covar = "score.compreensao.pre")
ds <- merge(ds[ds$variable != "score.compreensao.pre",],
ds[ds$variable == "score.compreensao.pre", !colnames(ds) %in% c("variable")],
by = c("grupo","genero"), all.x = T, suffixes = c("", ".score.compreensao.pre"))
ds <- merge(get_emmeans(pwcs[["grupo"]]), ds,
by = c("grupo","genero"), suffixes = c(".emms", ""))
ds <- ds[,c("grupo","genero","n","mean.score.compreensao.pre","se.score.compreensao.pre","mean","se",
"emmean","se.emms","conf.low","conf.high")]
colnames(ds) <- c("grupo","genero", "N", paste0(c("M","SE")," (pre)"),
paste0(c("M","SE"), " (unadj)"),
paste0(c("M", "SE"), " (adj)"), "conf.low", "conf.high")
lemms[["grupo:genero"]] <- ds
}
Computing
ANCOVA and PairWise After removing non-normal data (OK)
if (length(unique(pdat[["genero"]])) >= 2) {
wdat = pdat
res = residuals(lm(score.compreensao.pos ~ score.compreensao.pre + grupo*genero, data = wdat))
non.normal = getNonNormal(res, wdat$id, plimit = 0.05)
wdat = wdat[!wdat$id %in% non.normal,]
wdat.long <- rbind(wdat[,c("id","grupo","genero")], wdat[,c("id","grupo","genero")])
wdat.long[["time"]] <- c(rep("pre", nrow(wdat)), rep("pos", nrow(wdat)))
wdat.long[["time"]] <- factor(wdat.long[["time"]], c("pre","pos"))
wdat.long[["score.compreensao"]] <- c(wdat[["score.compreensao.pre"]], wdat[["score.compreensao.pos"]])
ldat[["grupo:genero"]] = wdat
(non.normal)
}
## NULL
if (length(unique(pdat[["genero"]])) >= 2) {
aov = anova_test(wdat, score.compreensao.pos ~ score.compreensao.pre + grupo*genero)
laov[["grupo:genero"]] <- merge(get_anova_table(aov), laov[["grupo:genero"]],
by="Effect", suffixes = c("","'"))
df = get_anova_table(aov)
}
| score.compreensao.pre |
1 |
263 |
269.342 |
0.000 |
* |
0.506 |
| grupo |
1 |
263 |
0.068 |
0.794 |
|
0.000 |
| genero |
1 |
263 |
4.429 |
0.036 |
* |
0.017 |
| grupo:genero |
1 |
263 |
0.218 |
0.641 |
|
0.001 |
if (length(unique(pdat[["genero"]])) >= 2) {
pwcs <- list()
pwcs[["genero"]] <- emmeans_test(
group_by(wdat, grupo), score.compreensao.pos ~ genero,
covariate = score.compreensao.pre, p.adjust.method = "bonferroni")
pwcs[["grupo"]] <- emmeans_test(
group_by(wdat, genero), score.compreensao.pos ~ grupo,
covariate = score.compreensao.pre, p.adjust.method = "bonferroni")
pwc <- plyr::rbind.fill(pwcs[["grupo"]], pwcs[["genero"]])
pwc <- pwc[,c("grupo","genero", colnames(pwc)[!colnames(pwc) %in% c("grupo","genero")])]
}
|
F |
score.compreensao.pre*grupo |
score.compreensao.pos |
Controle |
Experimental |
263 |
0.151 |
0.880 |
0.880 |
ns |
|
M |
score.compreensao.pre*grupo |
score.compreensao.pos |
Controle |
Experimental |
263 |
-0.513 |
0.608 |
0.608 |
ns |
| Controle |
|
score.compreensao.pre*genero |
score.compreensao.pos |
F |
M |
263 |
1.912 |
0.057 |
0.057 |
ns |
| Experimental |
|
score.compreensao.pre*genero |
score.compreensao.pos |
F |
M |
263 |
1.018 |
0.310 |
0.310 |
ns |
if (length(unique(pdat[["genero"]])) >= 2) {
pwc.long <- emmeans_test(dplyr::group_by_at(wdat.long, c("grupo","genero")),
score.compreensao ~ time,
p.adjust.method = "bonferroni")
lpwc[["grupo:genero"]] <- merge(plyr::rbind.fill(pwc, pwc.long),
lpwc[["grupo:genero"]],
by=c("grupo","genero","term",".y.","group1","group2"),
suffixes = c("","'"))
}
| Controle |
F |
time |
score.compreensao |
pre |
pos |
528 |
0.091 |
0.927 |
0.927 |
ns |
| Controle |
M |
time |
score.compreensao |
pre |
pos |
528 |
1.165 |
0.245 |
0.245 |
ns |
| Experimental |
F |
time |
score.compreensao |
pre |
pos |
528 |
0.767 |
0.443 |
0.443 |
ns |
| Experimental |
M |
time |
score.compreensao |
pre |
pos |
528 |
0.629 |
0.529 |
0.529 |
ns |
if (length(unique(pdat[["genero"]])) >= 2) {
ds <- get.descriptives(wdat, "score.compreensao.pos", c("grupo","genero"), covar = "score.compreensao.pre")
ds <- merge(ds[ds$variable != "score.compreensao.pre",],
ds[ds$variable == "score.compreensao.pre", !colnames(ds) %in% c("variable")],
by = c("grupo","genero"), all.x = T, suffixes = c("", ".score.compreensao.pre"))
ds <- merge(get_emmeans(pwcs[["grupo"]]), ds,
by = c("grupo","genero"), suffixes = c(".emms", ""))
ds <- ds[,c("grupo","genero","n","mean.score.compreensao.pre","se.score.compreensao.pre",
"mean","se","emmean","se.emms","conf.low","conf.high")]
colnames(ds) <- c("grupo","genero", "N", paste0(c("M","SE")," (pre)"),
paste0(c("M","SE"), " (unadj)"),
paste0(c("M", "SE"), " (adj)"), "conf.low", "conf.high")
lemms[["grupo:genero"]] <- merge(ds, lemms[["grupo:genero"]],
by=c("grupo","genero"), suffixes = c("","'"))
}
| Controle |
F |
79 |
5.291 |
0.298 |
5.253 |
0.290 |
5.245 |
0.208 |
4.835 |
5.655 |
| Controle |
M |
74 |
4.892 |
0.280 |
4.392 |
0.329 |
4.672 |
0.216 |
4.247 |
5.097 |
| Experimental |
F |
55 |
6.345 |
0.358 |
5.964 |
0.370 |
5.195 |
0.254 |
4.695 |
5.695 |
| Experimental |
M |
60 |
4.767 |
0.346 |
4.467 |
0.298 |
4.837 |
0.240 |
4.364 |
5.309 |
Plots for ancova
if (length(unique(pdat[["genero"]])) >= 2) {
ggPlotAoC2(pwcs, "grupo", "genero", aov, ylab = "Reading Comprehension",
subtitle = which(aov$Effect == "grupo:genero"), addParam = "errorbar") +
ggplot2::scale_color_manual(values = color[["genero"]]) +
ggplot2::ylab("Reading Comprehension") +
if (ymin.ci < ymax.ci) ggplot2::ylim(ymin.ci, ymax.ci)
}
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.

if (length(unique(pdat[["genero"]])) >= 2) {
ggPlotAoC2(pwcs, "genero", "grupo", aov, ylab = "Reading Comprehension",
subtitle = which(aov$Effect == "grupo:genero"), addParam = "errorbar") +
ggplot2::scale_color_manual(values = color[["grupo"]]) +
ggplot2::ylab("Reading Comprehension") +
if (ymin.ci < ymax.ci) ggplot2::ylim(ymin.ci, ymax.ci)
}
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.

if (length(unique(pdat[["genero"]])) >= 2) {
plots <- twoWayAncovaBoxPlots(
wdat, "score.compreensao.pos", c("grupo","genero"), aov, pwcs, covar = "score.compreensao.pre",
theme = "classic", color = color[["grupo:genero"]],
subtitle = which(aov$Effect == "grupo:genero"))
}
if (length(unique(pdat[["genero"]])) >= 2) {
plots[["grupo:genero"]] + ggplot2::ylab("Reading Comprehension") +
ggplot2::scale_x_discrete(labels=c('pre', 'pos')) +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}
## Warning: No shared levels found between `names(values)` of the manual scale and the data's colour values.

if (length(unique(pdat[["genero"]])) >= 2) {
plots <- twoWayAncovaBoxPlots(
wdat.long, "score.compreensao", c("grupo","genero"), aov, pwc.long,
pre.post = "time",
theme = "classic", color = color$prepost)
}
if (length(unique(pdat[["genero"]])) >= 2)
plots[["grupo:genero"]] + ggplot2::ylab("Reading Comprehension") +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)

Checking linearity
assumption
if (length(unique(pdat[["genero"]])) >= 2) {
ggscatter(wdat, x = "score.compreensao.pre", y = "score.compreensao.pos", size = 0.5,
facet.by = c("grupo","genero"), add = "reg.line")+
stat_regline_equation(
aes(label = paste(..eq.label.., ..rr.label.., sep = "~~~~"))
) + ggplot2::ylab("Reading Comprehension") +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}

if (length(unique(pdat[["genero"]])) >= 2) {
ggscatter(wdat, x = "score.compreensao.pre", y = "score.compreensao.pos", size = 0.5,
color = "grupo", facet.by = "genero", add = "reg.line")+
stat_regline_equation(
aes(label = paste(..eq.label.., ..rr.label.., sep = "~~~~"), color = grupo)
) +
ggplot2::labs(subtitle = rstatix::get_test_label(aov, detailed = T, row = which(aov$Effect == "grupo:genero"))) +
ggplot2::scale_color_manual(values = color[["grupo"]]) +
ggplot2::ylab("Reading Comprehension") +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}

if (length(unique(pdat[["genero"]])) >= 2) {
ggscatter(wdat, x = "score.compreensao.pre", y = "score.compreensao.pos", size = 0.5,
color = "genero", facet.by = "grupo", add = "reg.line")+
stat_regline_equation(
aes(label = paste(..eq.label.., ..rr.label.., sep = "~~~~"), color = genero)
) +
ggplot2::labs(subtitle = rstatix::get_test_label(aov, detailed = T, row = which(aov$Effect == "grupo:genero"))) +
ggplot2::scale_color_manual(values = color[["genero"]]) +
ggplot2::ylab("Reading Comprehension") +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}

Checking normality and
homogeneity
if (length(unique(pdat[["genero"]])) >= 2)
res <- augment(lm(score.compreensao.pos ~ score.compreensao.pre + grupo*genero, data = wdat))
if (length(unique(pdat[["genero"]])) >= 2)
shapiro_test(res$.resid)
## # A tibble: 1 × 3
## variable statistic p.value
## <chr> <dbl> <dbl>
## 1 res$.resid 0.993 0.246
if (length(unique(pdat[["genero"]])) >= 2)
levene_test(res, .resid ~ grupo*genero)
## # A tibble: 1 × 4
## df1 df2 statistic p
## <int> <int> <dbl> <dbl>
## 1 3 264 0.535 0.658
ANCOVA
and Pairwise for two factors
grupo:zona.participante
Without remove non-normal
data
pdat = remove_group_data(dat[!is.na(dat[["grupo"]]) & !is.na(dat[["zona.participante"]]),],
"score.compreensao.pos", c("grupo","zona.participante"))
pdat = pdat[pdat[["zona.participante"]] %in% do.call(
intersect, lapply(unique(pdat[["grupo"]]), FUN = function(x) {
unique(pdat[["zona.participante"]][which(pdat[["grupo"]] == x)])
})),]
pdat[["grupo"]] = factor(pdat[["grupo"]], level[["grupo"]])
pdat[["zona.participante"]] = factor(
pdat[["zona.participante"]],
level[["zona.participante"]][level[["zona.participante"]] %in% unique(pdat[["zona.participante"]])])
pdat.long <- rbind(pdat[,c("id","grupo","zona.participante")], pdat[,c("id","grupo","zona.participante")])
pdat.long[["time"]] <- c(rep("pre", nrow(pdat)), rep("pos", nrow(pdat)))
pdat.long[["time"]] <- factor(pdat.long[["time"]], c("pre","pos"))
pdat.long[["score.compreensao"]] <- c(pdat[["score.compreensao.pre"]], pdat[["score.compreensao.pos"]])
if (length(unique(pdat[["zona.participante"]])) >= 2) {
aov = anova_test(pdat, score.compreensao.pos ~ score.compreensao.pre + grupo*zona.participante)
laov[["grupo:zona.participante"]] <- get_anova_table(aov)
}
if (length(unique(pdat[["zona.participante"]])) >= 2) {
pwcs <- list()
pwcs[["zona.participante"]] <- emmeans_test(
group_by(pdat, grupo), score.compreensao.pos ~ zona.participante,
covariate = score.compreensao.pre, p.adjust.method = "bonferroni")
pwcs[["grupo"]] <- emmeans_test(
group_by(pdat, zona.participante), score.compreensao.pos ~ grupo,
covariate = score.compreensao.pre, p.adjust.method = "bonferroni")
pwc <- plyr::rbind.fill(pwcs[["grupo"]], pwcs[["zona.participante"]])
pwc <- pwc[,c("grupo","zona.participante", colnames(pwc)[!colnames(pwc) %in% c("grupo","zona.participante")])]
}
if (length(unique(pdat[["zona.participante"]])) >= 2) {
pwc.long <- emmeans_test(dplyr::group_by_at(pdat.long, c("grupo","zona.participante")),
score.compreensao ~ time,
p.adjust.method = "bonferroni")
lpwc[["grupo:zona.participante"]] <- plyr::rbind.fill(pwc, pwc.long)
}
if (length(unique(pdat[["zona.participante"]])) >= 2) {
ds <- get.descriptives(pdat, "score.compreensao.pos", c("grupo","zona.participante"), covar = "score.compreensao.pre")
ds <- merge(ds[ds$variable != "score.compreensao.pre",],
ds[ds$variable == "score.compreensao.pre", !colnames(ds) %in% c("variable")],
by = c("grupo","zona.participante"), all.x = T, suffixes = c("", ".score.compreensao.pre"))
ds <- merge(get_emmeans(pwcs[["grupo"]]), ds,
by = c("grupo","zona.participante"), suffixes = c(".emms", ""))
ds <- ds[,c("grupo","zona.participante","n","mean.score.compreensao.pre","se.score.compreensao.pre","mean","se",
"emmean","se.emms","conf.low","conf.high")]
colnames(ds) <- c("grupo","zona.participante", "N", paste0(c("M","SE")," (pre)"),
paste0(c("M","SE"), " (unadj)"),
paste0(c("M", "SE"), " (adj)"), "conf.low", "conf.high")
lemms[["grupo:zona.participante"]] <- ds
}
Computing
ANCOVA and PairWise After removing non-normal data (OK)
if (length(unique(pdat[["zona.participante"]])) >= 2) {
wdat = pdat
res = residuals(lm(score.compreensao.pos ~ score.compreensao.pre + grupo*zona.participante, data = wdat))
non.normal = getNonNormal(res, wdat$id, plimit = 0.05)
wdat = wdat[!wdat$id %in% non.normal,]
wdat.long <- rbind(wdat[,c("id","grupo","zona.participante")], wdat[,c("id","grupo","zona.participante")])
wdat.long[["time"]] <- c(rep("pre", nrow(wdat)), rep("pos", nrow(wdat)))
wdat.long[["time"]] <- factor(wdat.long[["time"]], c("pre","pos"))
wdat.long[["score.compreensao"]] <- c(wdat[["score.compreensao.pre"]], wdat[["score.compreensao.pos"]])
ldat[["grupo:zona.participante"]] = wdat
(non.normal)
}
## NULL
if (length(unique(pdat[["zona.participante"]])) >= 2) {
aov = anova_test(wdat, score.compreensao.pos ~ score.compreensao.pre + grupo*zona.participante)
laov[["grupo:zona.participante"]] <- merge(get_anova_table(aov), laov[["grupo:zona.participante"]],
by="Effect", suffixes = c("","'"))
df = get_anova_table(aov)
}
| score.compreensao.pre |
1 |
202 |
208.042 |
0.000 |
* |
0.507 |
| grupo |
1 |
202 |
0.353 |
0.553 |
|
0.002 |
| zona.participante |
1 |
202 |
1.692 |
0.195 |
|
0.008 |
| grupo:zona.participante |
1 |
202 |
0.081 |
0.777 |
|
0.000 |
if (length(unique(pdat[["zona.participante"]])) >= 2) {
pwcs <- list()
pwcs[["zona.participante"]] <- emmeans_test(
group_by(wdat, grupo), score.compreensao.pos ~ zona.participante,
covariate = score.compreensao.pre, p.adjust.method = "bonferroni")
pwcs[["grupo"]] <- emmeans_test(
group_by(wdat, zona.participante), score.compreensao.pos ~ grupo,
covariate = score.compreensao.pre, p.adjust.method = "bonferroni")
pwc <- plyr::rbind.fill(pwcs[["grupo"]], pwcs[["zona.participante"]])
pwc <- pwc[,c("grupo","zona.participante", colnames(pwc)[!colnames(pwc) %in% c("grupo","zona.participante")])]
}
|
Rural |
score.compreensao.pre*grupo |
score.compreensao.pos |
Controle |
Experimental |
202 |
-0.616 |
0.539 |
0.539 |
ns |
|
Urbana |
score.compreensao.pre*grupo |
score.compreensao.pos |
Controle |
Experimental |
202 |
-0.234 |
0.815 |
0.815 |
ns |
| Controle |
|
score.compreensao.pre*zona.participante |
score.compreensao.pos |
Rural |
Urbana |
202 |
-1.164 |
0.246 |
0.246 |
ns |
| Experimental |
|
score.compreensao.pre*zona.participante |
score.compreensao.pos |
Rural |
Urbana |
202 |
-0.644 |
0.520 |
0.520 |
ns |
if (length(unique(pdat[["zona.participante"]])) >= 2) {
pwc.long <- emmeans_test(dplyr::group_by_at(wdat.long, c("grupo","zona.participante")),
score.compreensao ~ time,
p.adjust.method = "bonferroni")
lpwc[["grupo:zona.participante"]] <- merge(plyr::rbind.fill(pwc, pwc.long),
lpwc[["grupo:zona.participante"]],
by=c("grupo","zona.participante","term",".y.","group1","group2"),
suffixes = c("","'"))
}
| Controle |
Rural |
time |
score.compreensao |
pre |
pos |
406 |
0.491 |
0.623 |
0.623 |
ns |
| Controle |
Urbana |
time |
score.compreensao |
pre |
pos |
406 |
0.000 |
1.000 |
1.000 |
ns |
| Experimental |
Rural |
time |
score.compreensao |
pre |
pos |
406 |
0.438 |
0.662 |
0.662 |
ns |
| Experimental |
Urbana |
time |
score.compreensao |
pre |
pos |
406 |
-0.124 |
0.902 |
0.902 |
ns |
if (length(unique(pdat[["zona.participante"]])) >= 2) {
ds <- get.descriptives(wdat, "score.compreensao.pos", c("grupo","zona.participante"), covar = "score.compreensao.pre")
ds <- merge(ds[ds$variable != "score.compreensao.pre",],
ds[ds$variable == "score.compreensao.pre", !colnames(ds) %in% c("variable")],
by = c("grupo","zona.participante"), all.x = T, suffixes = c("", ".score.compreensao.pre"))
ds <- merge(get_emmeans(pwcs[["grupo"]]), ds,
by = c("grupo","zona.participante"), suffixes = c(".emms", ""))
ds <- ds[,c("grupo","zona.participante","n","mean.score.compreensao.pre","se.score.compreensao.pre",
"mean","se","emmean","se.emms","conf.low","conf.high")]
colnames(ds) <- c("grupo","zona.participante", "N", paste0(c("M","SE")," (pre)"),
paste0(c("M","SE"), " (unadj)"),
paste0(c("M", "SE"), " (adj)"), "conf.low", "conf.high")
lemms[["grupo:zona.participante"]] <- merge(ds, lemms[["grupo:zona.participante"]],
by=c("grupo","zona.participante"), suffixes = c("","'"))
}
| Controle |
Rural |
51 |
4.608 |
0.330 |
4.353 |
0.370 |
4.732 |
0.264 |
4.211 |
5.253 |
| Controle |
Urbana |
67 |
5.194 |
0.298 |
5.194 |
0.329 |
5.140 |
0.229 |
4.688 |
5.592 |
| Experimental |
Rural |
46 |
5.457 |
0.403 |
5.217 |
0.402 |
4.969 |
0.277 |
4.422 |
5.516 |
| Experimental |
Urbana |
43 |
5.256 |
0.430 |
5.326 |
0.396 |
5.226 |
0.286 |
4.661 |
5.790 |
Plots for ancova
if (length(unique(pdat[["zona.participante"]])) >= 2) {
ggPlotAoC2(pwcs, "grupo", "zona.participante", aov, ylab = "Reading Comprehension",
subtitle = which(aov$Effect == "grupo:zona.participante"), addParam = "errorbar") +
ggplot2::scale_color_manual(values = color[["zona.participante"]]) +
ggplot2::ylab("Reading Comprehension") +
if (ymin.ci < ymax.ci) ggplot2::ylim(ymin.ci, ymax.ci)
}
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.

if (length(unique(pdat[["zona.participante"]])) >= 2) {
ggPlotAoC2(pwcs, "zona.participante", "grupo", aov, ylab = "Reading Comprehension",
subtitle = which(aov$Effect == "grupo:zona.participante"), addParam = "errorbar") +
ggplot2::scale_color_manual(values = color[["grupo"]]) +
ggplot2::ylab("Reading Comprehension") +
if (ymin.ci < ymax.ci) ggplot2::ylim(ymin.ci, ymax.ci)
}
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.

if (length(unique(pdat[["zona.participante"]])) >= 2) {
plots <- twoWayAncovaBoxPlots(
wdat, "score.compreensao.pos", c("grupo","zona.participante"), aov, pwcs, covar = "score.compreensao.pre",
theme = "classic", color = color[["grupo:zona.participante"]],
subtitle = which(aov$Effect == "grupo:zona.participante"))
}
if (length(unique(pdat[["zona.participante"]])) >= 2) {
plots[["grupo:zona.participante"]] + ggplot2::ylab("Reading Comprehension") +
ggplot2::scale_x_discrete(labels=c('pre', 'pos')) +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}
## Warning: No shared levels found between `names(values)` of the manual scale and the data's colour values.

if (length(unique(pdat[["zona.participante"]])) >= 2) {
plots <- twoWayAncovaBoxPlots(
wdat.long, "score.compreensao", c("grupo","zona.participante"), aov, pwc.long,
pre.post = "time",
theme = "classic", color = color$prepost)
}
if (length(unique(pdat[["zona.participante"]])) >= 2)
plots[["grupo:zona.participante"]] + ggplot2::ylab("Reading Comprehension") +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)

Checking linearity
assumption
if (length(unique(pdat[["zona.participante"]])) >= 2) {
ggscatter(wdat, x = "score.compreensao.pre", y = "score.compreensao.pos", size = 0.5,
facet.by = c("grupo","zona.participante"), add = "reg.line")+
stat_regline_equation(
aes(label = paste(..eq.label.., ..rr.label.., sep = "~~~~"))
) + ggplot2::ylab("Reading Comprehension") +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}

if (length(unique(pdat[["zona.participante"]])) >= 2) {
ggscatter(wdat, x = "score.compreensao.pre", y = "score.compreensao.pos", size = 0.5,
color = "grupo", facet.by = "zona.participante", add = "reg.line")+
stat_regline_equation(
aes(label = paste(..eq.label.., ..rr.label.., sep = "~~~~"), color = grupo)
) +
ggplot2::labs(subtitle = rstatix::get_test_label(aov, detailed = T, row = which(aov$Effect == "grupo:zona.participante"))) +
ggplot2::scale_color_manual(values = color[["grupo"]]) +
ggplot2::ylab("Reading Comprehension") +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}

if (length(unique(pdat[["zona.participante"]])) >= 2) {
ggscatter(wdat, x = "score.compreensao.pre", y = "score.compreensao.pos", size = 0.5,
color = "zona.participante", facet.by = "grupo", add = "reg.line")+
stat_regline_equation(
aes(label = paste(..eq.label.., ..rr.label.., sep = "~~~~"), color = zona.participante)
) +
ggplot2::labs(subtitle = rstatix::get_test_label(aov, detailed = T, row = which(aov$Effect == "grupo:zona.participante"))) +
ggplot2::scale_color_manual(values = color[["zona.participante"]]) +
ggplot2::ylab("Reading Comprehension") +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}

Checking normality and
homogeneity
if (length(unique(pdat[["zona.participante"]])) >= 2)
res <- augment(lm(score.compreensao.pos ~ score.compreensao.pre + grupo*zona.participante, data = wdat))
if (length(unique(pdat[["zona.participante"]])) >= 2)
shapiro_test(res$.resid)
## # A tibble: 1 × 3
## variable statistic p.value
## <chr> <dbl> <dbl>
## 1 res$.resid 0.992 0.312
if (length(unique(pdat[["zona.participante"]])) >= 2)
levene_test(res, .resid ~ grupo*zona.participante)
## # A tibble: 1 × 4
## df1 df2 statistic p
## <int> <int> <dbl> <dbl>
## 1 3 203 0.327 0.806
ANCOVA and
Pairwise for two factors grupo:zona.escola
Without remove non-normal
data
pdat = remove_group_data(dat[!is.na(dat[["grupo"]]) & !is.na(dat[["zona.escola"]]),],
"score.compreensao.pos", c("grupo","zona.escola"))
pdat = pdat[pdat[["zona.escola"]] %in% do.call(
intersect, lapply(unique(pdat[["grupo"]]), FUN = function(x) {
unique(pdat[["zona.escola"]][which(pdat[["grupo"]] == x)])
})),]
pdat[["grupo"]] = factor(pdat[["grupo"]], level[["grupo"]])
pdat[["zona.escola"]] = factor(
pdat[["zona.escola"]],
level[["zona.escola"]][level[["zona.escola"]] %in% unique(pdat[["zona.escola"]])])
pdat.long <- rbind(pdat[,c("id","grupo","zona.escola")], pdat[,c("id","grupo","zona.escola")])
pdat.long[["time"]] <- c(rep("pre", nrow(pdat)), rep("pos", nrow(pdat)))
pdat.long[["time"]] <- factor(pdat.long[["time"]], c("pre","pos"))
pdat.long[["score.compreensao"]] <- c(pdat[["score.compreensao.pre"]], pdat[["score.compreensao.pos"]])
if (length(unique(pdat[["zona.escola"]])) >= 2) {
aov = anova_test(pdat, score.compreensao.pos ~ score.compreensao.pre + grupo*zona.escola)
laov[["grupo:zona.escola"]] <- get_anova_table(aov)
}
if (length(unique(pdat[["zona.escola"]])) >= 2) {
pwcs <- list()
pwcs[["zona.escola"]] <- emmeans_test(
group_by(pdat, grupo), score.compreensao.pos ~ zona.escola,
covariate = score.compreensao.pre, p.adjust.method = "bonferroni")
pwcs[["grupo"]] <- emmeans_test(
group_by(pdat, zona.escola), score.compreensao.pos ~ grupo,
covariate = score.compreensao.pre, p.adjust.method = "bonferroni")
pwc <- plyr::rbind.fill(pwcs[["grupo"]], pwcs[["zona.escola"]])
pwc <- pwc[,c("grupo","zona.escola", colnames(pwc)[!colnames(pwc) %in% c("grupo","zona.escola")])]
}
if (length(unique(pdat[["zona.escola"]])) >= 2) {
pwc.long <- emmeans_test(dplyr::group_by_at(pdat.long, c("grupo","zona.escola")),
score.compreensao ~ time,
p.adjust.method = "bonferroni")
lpwc[["grupo:zona.escola"]] <- plyr::rbind.fill(pwc, pwc.long)
}
if (length(unique(pdat[["zona.escola"]])) >= 2) {
ds <- get.descriptives(pdat, "score.compreensao.pos", c("grupo","zona.escola"), covar = "score.compreensao.pre")
ds <- merge(ds[ds$variable != "score.compreensao.pre",],
ds[ds$variable == "score.compreensao.pre", !colnames(ds) %in% c("variable")],
by = c("grupo","zona.escola"), all.x = T, suffixes = c("", ".score.compreensao.pre"))
ds <- merge(get_emmeans(pwcs[["grupo"]]), ds,
by = c("grupo","zona.escola"), suffixes = c(".emms", ""))
ds <- ds[,c("grupo","zona.escola","n","mean.score.compreensao.pre","se.score.compreensao.pre","mean","se",
"emmean","se.emms","conf.low","conf.high")]
colnames(ds) <- c("grupo","zona.escola", "N", paste0(c("M","SE")," (pre)"),
paste0(c("M","SE"), " (unadj)"),
paste0(c("M", "SE"), " (adj)"), "conf.low", "conf.high")
lemms[["grupo:zona.escola"]] <- ds
}
Computing
ANCOVA and PairWise After removing non-normal data (OK)
if (length(unique(pdat[["zona.escola"]])) >= 2) {
wdat = pdat
res = residuals(lm(score.compreensao.pos ~ score.compreensao.pre + grupo*zona.escola, data = wdat))
non.normal = getNonNormal(res, wdat$id, plimit = 0.05)
wdat = wdat[!wdat$id %in% non.normal,]
wdat.long <- rbind(wdat[,c("id","grupo","zona.escola")], wdat[,c("id","grupo","zona.escola")])
wdat.long[["time"]] <- c(rep("pre", nrow(wdat)), rep("pos", nrow(wdat)))
wdat.long[["time"]] <- factor(wdat.long[["time"]], c("pre","pos"))
wdat.long[["score.compreensao"]] <- c(wdat[["score.compreensao.pre"]], wdat[["score.compreensao.pos"]])
ldat[["grupo:zona.escola"]] = wdat
(non.normal)
}
## NULL
if (length(unique(pdat[["zona.escola"]])) >= 2) {
aov = anova_test(wdat, score.compreensao.pos ~ score.compreensao.pre + grupo*zona.escola)
laov[["grupo:zona.escola"]] <- merge(get_anova_table(aov), laov[["grupo:zona.escola"]],
by="Effect", suffixes = c("","'"))
df = get_anova_table(aov)
}
| score.compreensao.pre |
1 |
263 |
284.478 |
0.000 |
* |
0.520 |
| grupo |
1 |
263 |
0.012 |
0.913 |
|
0.000 |
| zona.escola |
1 |
263 |
0.643 |
0.423 |
|
0.002 |
| grupo:zona.escola |
1 |
263 |
0.169 |
0.682 |
|
0.001 |
if (length(unique(pdat[["zona.escola"]])) >= 2) {
pwcs <- list()
pwcs[["zona.escola"]] <- emmeans_test(
group_by(wdat, grupo), score.compreensao.pos ~ zona.escola,
covariate = score.compreensao.pre, p.adjust.method = "bonferroni")
pwcs[["grupo"]] <- emmeans_test(
group_by(wdat, zona.escola), score.compreensao.pos ~ grupo,
covariate = score.compreensao.pre, p.adjust.method = "bonferroni")
pwc <- plyr::rbind.fill(pwcs[["grupo"]], pwcs[["zona.escola"]])
pwc <- pwc[,c("grupo","zona.escola", colnames(pwc)[!colnames(pwc) %in% c("grupo","zona.escola")])]
}
|
Rural |
score.compreensao.pre*grupo |
score.compreensao.pos |
Controle |
Experimental |
263 |
0.277 |
0.782 |
0.782 |
ns |
|
Urbana |
score.compreensao.pre*grupo |
score.compreensao.pos |
Controle |
Experimental |
263 |
-0.322 |
0.748 |
0.748 |
ns |
| Controle |
|
score.compreensao.pre*zona.escola |
score.compreensao.pos |
Rural |
Urbana |
263 |
-0.355 |
0.723 |
0.723 |
ns |
| Experimental |
|
score.compreensao.pre*zona.escola |
score.compreensao.pos |
Rural |
Urbana |
263 |
-0.833 |
0.406 |
0.406 |
ns |
if (length(unique(pdat[["zona.escola"]])) >= 2) {
pwc.long <- emmeans_test(dplyr::group_by_at(wdat.long, c("grupo","zona.escola")),
score.compreensao ~ time,
p.adjust.method = "bonferroni")
lpwc[["grupo:zona.escola"]] <- merge(plyr::rbind.fill(pwc, pwc.long),
lpwc[["grupo:zona.escola"]],
by=c("grupo","zona.escola","term",".y.","group1","group2"),
suffixes = c("","'"))
}
| Controle |
Rural |
time |
score.compreensao |
pre |
pos |
528 |
0.925 |
0.355 |
0.355 |
ns |
| Controle |
Urbana |
time |
score.compreensao |
pre |
pos |
528 |
0.398 |
0.690 |
0.690 |
ns |
| Experimental |
Rural |
time |
score.compreensao |
pre |
pos |
528 |
1.173 |
0.241 |
0.241 |
ns |
| Experimental |
Urbana |
time |
score.compreensao |
pre |
pos |
528 |
0.388 |
0.698 |
0.698 |
ns |
if (length(unique(pdat[["zona.escola"]])) >= 2) {
ds <- get.descriptives(wdat, "score.compreensao.pos", c("grupo","zona.escola"), covar = "score.compreensao.pre")
ds <- merge(ds[ds$variable != "score.compreensao.pre",],
ds[ds$variable == "score.compreensao.pre", !colnames(ds) %in% c("variable")],
by = c("grupo","zona.escola"), all.x = T, suffixes = c("", ".score.compreensao.pre"))
ds <- merge(get_emmeans(pwcs[["grupo"]]), ds,
by = c("grupo","zona.escola"), suffixes = c(".emms", ""))
ds <- ds[,c("grupo","zona.escola","n","mean.score.compreensao.pre","se.score.compreensao.pre",
"mean","se","emmean","se.emms","conf.low","conf.high")]
colnames(ds) <- c("grupo","zona.escola", "N", paste0(c("M","SE")," (pre)"),
paste0(c("M","SE"), " (unadj)"),
paste0(c("M", "SE"), " (adj)"), "conf.low", "conf.high")
lemms[["grupo:zona.escola"]] <- merge(ds, lemms[["grupo:zona.escola"]],
by=c("grupo","zona.escola"), suffixes = c("","'"))
}
| Controle |
Rural |
52 |
5.654 |
0.326 |
5.173 |
0.378 |
4.896 |
0.259 |
4.386 |
5.406 |
| Controle |
Urbana |
101 |
4.812 |
0.257 |
4.663 |
0.271 |
5.010 |
0.187 |
4.642 |
5.377 |
| Experimental |
Rural |
35 |
6.229 |
0.462 |
5.486 |
0.482 |
4.783 |
0.318 |
4.157 |
5.409 |
| Experimental |
Urbana |
80 |
5.213 |
0.307 |
5.050 |
0.282 |
5.100 |
0.208 |
4.689 |
5.510 |
Plots for ancova
if (length(unique(pdat[["zona.escola"]])) >= 2) {
ggPlotAoC2(pwcs, "grupo", "zona.escola", aov, ylab = "Reading Comprehension",
subtitle = which(aov$Effect == "grupo:zona.escola"), addParam = "errorbar") +
ggplot2::scale_color_manual(values = color[["zona.escola"]]) +
ggplot2::ylab("Reading Comprehension") +
if (ymin.ci < ymax.ci) ggplot2::ylim(ymin.ci, ymax.ci)
}
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.

if (length(unique(pdat[["zona.escola"]])) >= 2) {
ggPlotAoC2(pwcs, "zona.escola", "grupo", aov, ylab = "Reading Comprehension",
subtitle = which(aov$Effect == "grupo:zona.escola"), addParam = "errorbar") +
ggplot2::scale_color_manual(values = color[["grupo"]]) +
ggplot2::ylab("Reading Comprehension") +
if (ymin.ci < ymax.ci) ggplot2::ylim(ymin.ci, ymax.ci)
}
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.

if (length(unique(pdat[["zona.escola"]])) >= 2) {
plots <- twoWayAncovaBoxPlots(
wdat, "score.compreensao.pos", c("grupo","zona.escola"), aov, pwcs, covar = "score.compreensao.pre",
theme = "classic", color = color[["grupo:zona.escola"]],
subtitle = which(aov$Effect == "grupo:zona.escola"))
}
if (length(unique(pdat[["zona.escola"]])) >= 2) {
plots[["grupo:zona.escola"]] + ggplot2::ylab("Reading Comprehension") +
ggplot2::scale_x_discrete(labels=c('pre', 'pos')) +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}
## Warning: No shared levels found between `names(values)` of the manual scale and the data's colour values.

if (length(unique(pdat[["zona.escola"]])) >= 2) {
plots <- twoWayAncovaBoxPlots(
wdat.long, "score.compreensao", c("grupo","zona.escola"), aov, pwc.long,
pre.post = "time",
theme = "classic", color = color$prepost)
}
if (length(unique(pdat[["zona.escola"]])) >= 2)
plots[["grupo:zona.escola"]] + ggplot2::ylab("Reading Comprehension") +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)

Checking linearity
assumption
if (length(unique(pdat[["zona.escola"]])) >= 2) {
ggscatter(wdat, x = "score.compreensao.pre", y = "score.compreensao.pos", size = 0.5,
facet.by = c("grupo","zona.escola"), add = "reg.line")+
stat_regline_equation(
aes(label = paste(..eq.label.., ..rr.label.., sep = "~~~~"))
) + ggplot2::ylab("Reading Comprehension") +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}

if (length(unique(pdat[["zona.escola"]])) >= 2) {
ggscatter(wdat, x = "score.compreensao.pre", y = "score.compreensao.pos", size = 0.5,
color = "grupo", facet.by = "zona.escola", add = "reg.line")+
stat_regline_equation(
aes(label = paste(..eq.label.., ..rr.label.., sep = "~~~~"), color = grupo)
) +
ggplot2::labs(subtitle = rstatix::get_test_label(aov, detailed = T, row = which(aov$Effect == "grupo:zona.escola"))) +
ggplot2::scale_color_manual(values = color[["grupo"]]) +
ggplot2::ylab("Reading Comprehension") +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}

if (length(unique(pdat[["zona.escola"]])) >= 2) {
ggscatter(wdat, x = "score.compreensao.pre", y = "score.compreensao.pos", size = 0.5,
color = "zona.escola", facet.by = "grupo", add = "reg.line")+
stat_regline_equation(
aes(label = paste(..eq.label.., ..rr.label.., sep = "~~~~"), color = zona.escola)
) +
ggplot2::labs(subtitle = rstatix::get_test_label(aov, detailed = T, row = which(aov$Effect == "grupo:zona.escola"))) +
ggplot2::scale_color_manual(values = color[["zona.escola"]]) +
ggplot2::ylab("Reading Comprehension") +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}

Checking normality and
homogeneity
if (length(unique(pdat[["zona.escola"]])) >= 2)
res <- augment(lm(score.compreensao.pos ~ score.compreensao.pre + grupo*zona.escola, data = wdat))
if (length(unique(pdat[["zona.escola"]])) >= 2)
shapiro_test(res$.resid)
## # A tibble: 1 × 3
## variable statistic p.value
## <chr> <dbl> <dbl>
## 1 res$.resid 0.994 0.315
if (length(unique(pdat[["zona.escola"]])) >= 2)
levene_test(res, .resid ~ grupo*zona.escola)
## # A tibble: 1 × 4
## df1 df2 statistic p
## <int> <int> <dbl> <dbl>
## 1 3 264 0.271 0.846
ANCOVA
and Pairwise for two factors
grupo:score.compreensao.quintile
Without remove non-normal
data
pdat = remove_group_data(dat[!is.na(dat[["grupo"]]) & !is.na(dat[["score.compreensao.quintile"]]),],
"score.compreensao.pos", c("grupo","score.compreensao.quintile"))
pdat = pdat[pdat[["score.compreensao.quintile"]] %in% do.call(
intersect, lapply(unique(pdat[["grupo"]]), FUN = function(x) {
unique(pdat[["score.compreensao.quintile"]][which(pdat[["grupo"]] == x)])
})),]
pdat[["grupo"]] = factor(pdat[["grupo"]], level[["grupo"]])
pdat[["score.compreensao.quintile"]] = factor(
pdat[["score.compreensao.quintile"]],
level[["score.compreensao.quintile"]][level[["score.compreensao.quintile"]] %in% unique(pdat[["score.compreensao.quintile"]])])
pdat.long <- rbind(pdat[,c("id","grupo","score.compreensao.quintile")], pdat[,c("id","grupo","score.compreensao.quintile")])
pdat.long[["time"]] <- c(rep("pre", nrow(pdat)), rep("pos", nrow(pdat)))
pdat.long[["time"]] <- factor(pdat.long[["time"]], c("pre","pos"))
pdat.long[["score.compreensao"]] <- c(pdat[["score.compreensao.pre"]], pdat[["score.compreensao.pos"]])
if (length(unique(pdat[["score.compreensao.quintile"]])) >= 2) {
aov = anova_test(pdat, score.compreensao.pos ~ score.compreensao.pre + grupo*score.compreensao.quintile)
laov[["grupo:score.compreensao.quintile"]] <- get_anova_table(aov)
}
if (length(unique(pdat[["score.compreensao.quintile"]])) >= 2) {
pwcs <- list()
pwcs[["score.compreensao.quintile"]] <- emmeans_test(
group_by(pdat, grupo), score.compreensao.pos ~ score.compreensao.quintile,
covariate = score.compreensao.pre, p.adjust.method = "bonferroni")
pwcs[["grupo"]] <- emmeans_test(
group_by(pdat, score.compreensao.quintile), score.compreensao.pos ~ grupo,
covariate = score.compreensao.pre, p.adjust.method = "bonferroni")
pwc <- plyr::rbind.fill(pwcs[["grupo"]], pwcs[["score.compreensao.quintile"]])
pwc <- pwc[,c("grupo","score.compreensao.quintile", colnames(pwc)[!colnames(pwc) %in% c("grupo","score.compreensao.quintile")])]
}
if (length(unique(pdat[["score.compreensao.quintile"]])) >= 2) {
pwc.long <- emmeans_test(dplyr::group_by_at(pdat.long, c("grupo","score.compreensao.quintile")),
score.compreensao ~ time,
p.adjust.method = "bonferroni")
lpwc[["grupo:score.compreensao.quintile"]] <- plyr::rbind.fill(pwc, pwc.long)
}
if (length(unique(pdat[["score.compreensao.quintile"]])) >= 2) {
ds <- get.descriptives(pdat, "score.compreensao.pos", c("grupo","score.compreensao.quintile"), covar = "score.compreensao.pre")
ds <- merge(ds[ds$variable != "score.compreensao.pre",],
ds[ds$variable == "score.compreensao.pre", !colnames(ds) %in% c("variable")],
by = c("grupo","score.compreensao.quintile"), all.x = T, suffixes = c("", ".score.compreensao.pre"))
ds <- merge(get_emmeans(pwcs[["grupo"]]), ds,
by = c("grupo","score.compreensao.quintile"), suffixes = c(".emms", ""))
ds <- ds[,c("grupo","score.compreensao.quintile","n","mean.score.compreensao.pre","se.score.compreensao.pre","mean","se",
"emmean","se.emms","conf.low","conf.high")]
colnames(ds) <- c("grupo","score.compreensao.quintile", "N", paste0(c("M","SE")," (pre)"),
paste0(c("M","SE"), " (unadj)"),
paste0(c("M", "SE"), " (adj)"), "conf.low", "conf.high")
lemms[["grupo:score.compreensao.quintile"]] <- ds
}
Computing
ANCOVA and PairWise After removing non-normal data (OK)
if (length(unique(pdat[["score.compreensao.quintile"]])) >= 2) {
wdat = pdat
res = residuals(lm(score.compreensao.pos ~ score.compreensao.pre + grupo*score.compreensao.quintile, data = wdat))
non.normal = getNonNormal(res, wdat$id, plimit = 0.05)
wdat = wdat[!wdat$id %in% non.normal,]
wdat.long <- rbind(wdat[,c("id","grupo","score.compreensao.quintile")], wdat[,c("id","grupo","score.compreensao.quintile")])
wdat.long[["time"]] <- c(rep("pre", nrow(wdat)), rep("pos", nrow(wdat)))
wdat.long[["time"]] <- factor(wdat.long[["time"]], c("pre","pos"))
wdat.long[["score.compreensao"]] <- c(wdat[["score.compreensao.pre"]], wdat[["score.compreensao.pos"]])
ldat[["grupo:score.compreensao.quintile"]] = wdat
(non.normal)
}
## NULL
if (length(unique(pdat[["score.compreensao.quintile"]])) >= 2) {
aov = anova_test(wdat, score.compreensao.pos ~ score.compreensao.pre + grupo*score.compreensao.quintile)
laov[["grupo:score.compreensao.quintile"]] <- merge(get_anova_table(aov), laov[["grupo:score.compreensao.quintile"]],
by="Effect", suffixes = c("","'"))
df = get_anova_table(aov)
}
| score.compreensao.pre |
1 |
257 |
18.769 |
0.000 |
* |
0.068 |
| grupo |
1 |
257 |
0.056 |
0.814 |
|
0.000 |
| score.compreensao.quintile |
4 |
257 |
4.226 |
0.002 |
* |
0.062 |
| grupo:score.compreensao.quintile |
4 |
257 |
0.489 |
0.744 |
|
0.008 |
if (length(unique(pdat[["score.compreensao.quintile"]])) >= 2) {
pwcs <- list()
pwcs[["score.compreensao.quintile"]] <- emmeans_test(
group_by(wdat, grupo), score.compreensao.pos ~ score.compreensao.quintile,
covariate = score.compreensao.pre, p.adjust.method = "bonferroni")
pwcs[["grupo"]] <- emmeans_test(
group_by(wdat, score.compreensao.quintile), score.compreensao.pos ~ grupo,
covariate = score.compreensao.pre, p.adjust.method = "bonferroni")
pwc <- plyr::rbind.fill(pwcs[["grupo"]], pwcs[["score.compreensao.quintile"]])
pwc <- pwc[,c("grupo","score.compreensao.quintile", colnames(pwc)[!colnames(pwc) %in% c("grupo","score.compreensao.quintile")])]
}
|
1st quintile |
score.compreensao.pre*grupo |
score.compreensao.pos |
Controle |
Experimental |
257 |
-0.963 |
0.336 |
0.336 |
ns |
|
2nd quintile |
score.compreensao.pre*grupo |
score.compreensao.pos |
Controle |
Experimental |
257 |
-0.297 |
0.767 |
0.767 |
ns |
|
3rd quintile |
score.compreensao.pre*grupo |
score.compreensao.pos |
Controle |
Experimental |
257 |
0.243 |
0.808 |
0.808 |
ns |
|
4th quintile |
score.compreensao.pre*grupo |
score.compreensao.pos |
Controle |
Experimental |
257 |
0.966 |
0.335 |
0.335 |
ns |
|
5th quintile |
score.compreensao.pre*grupo |
score.compreensao.pos |
Controle |
Experimental |
257 |
0.059 |
0.953 |
0.953 |
ns |
| Controle |
|
score.compreensao.pre*score.compreensao.quintile |
score.compreensao.pos |
1st quintile |
2nd quintile |
257 |
0.961 |
0.337 |
1.000 |
ns |
| Controle |
|
score.compreensao.pre*score.compreensao.quintile |
score.compreensao.pos |
1st quintile |
3rd quintile |
257 |
1.269 |
0.206 |
1.000 |
ns |
| Controle |
|
score.compreensao.pre*score.compreensao.quintile |
score.compreensao.pos |
1st quintile |
4th quintile |
257 |
0.586 |
0.558 |
1.000 |
ns |
| Controle |
|
score.compreensao.pre*score.compreensao.quintile |
score.compreensao.pos |
1st quintile |
5th quintile |
257 |
0.343 |
0.732 |
1.000 |
ns |
| Controle |
|
score.compreensao.pre*score.compreensao.quintile |
score.compreensao.pos |
2nd quintile |
3rd quintile |
257 |
0.962 |
0.337 |
1.000 |
ns |
| Controle |
|
score.compreensao.pre*score.compreensao.quintile |
score.compreensao.pos |
2nd quintile |
4th quintile |
257 |
0.026 |
0.979 |
1.000 |
ns |
| Controle |
|
score.compreensao.pre*score.compreensao.quintile |
score.compreensao.pos |
2nd quintile |
5th quintile |
257 |
-0.142 |
0.887 |
1.000 |
ns |
| Controle |
|
score.compreensao.pre*score.compreensao.quintile |
score.compreensao.pos |
3rd quintile |
4th quintile |
257 |
-0.787 |
0.432 |
1.000 |
ns |
| Controle |
|
score.compreensao.pre*score.compreensao.quintile |
score.compreensao.pos |
3rd quintile |
5th quintile |
257 |
-0.715 |
0.475 |
1.000 |
ns |
| Controle |
|
score.compreensao.pre*score.compreensao.quintile |
score.compreensao.pos |
4th quintile |
5th quintile |
257 |
-0.321 |
0.749 |
1.000 |
ns |
| Experimental |
|
score.compreensao.pre*score.compreensao.quintile |
score.compreensao.pos |
1st quintile |
2nd quintile |
257 |
2.104 |
0.036 |
0.364 |
ns |
| Experimental |
|
score.compreensao.pre*score.compreensao.quintile |
score.compreensao.pos |
1st quintile |
3rd quintile |
257 |
2.389 |
0.018 |
0.176 |
ns |
| Experimental |
|
score.compreensao.pre*score.compreensao.quintile |
score.compreensao.pos |
1st quintile |
4th quintile |
257 |
1.657 |
0.099 |
0.987 |
ns |
| Experimental |
|
score.compreensao.pre*score.compreensao.quintile |
score.compreensao.pos |
1st quintile |
5th quintile |
257 |
0.924 |
0.356 |
1.000 |
ns |
| Experimental |
|
score.compreensao.pre*score.compreensao.quintile |
score.compreensao.pos |
2nd quintile |
3rd quintile |
257 |
1.092 |
0.276 |
1.000 |
ns |
| Experimental |
|
score.compreensao.pre*score.compreensao.quintile |
score.compreensao.pos |
2nd quintile |
4th quintile |
257 |
0.613 |
0.540 |
1.000 |
ns |
| Experimental |
|
score.compreensao.pre*score.compreensao.quintile |
score.compreensao.pos |
2nd quintile |
5th quintile |
257 |
-0.012 |
0.991 |
1.000 |
ns |
| Experimental |
|
score.compreensao.pre*score.compreensao.quintile |
score.compreensao.pos |
3rd quintile |
4th quintile |
257 |
-0.150 |
0.881 |
1.000 |
ns |
| Experimental |
|
score.compreensao.pre*score.compreensao.quintile |
score.compreensao.pos |
3rd quintile |
5th quintile |
257 |
-0.805 |
0.422 |
1.000 |
ns |
| Experimental |
|
score.compreensao.pre*score.compreensao.quintile |
score.compreensao.pos |
4th quintile |
5th quintile |
257 |
-1.014 |
0.312 |
1.000 |
ns |
if (length(unique(pdat[["score.compreensao.quintile"]])) >= 2) {
pwc.long <- emmeans_test(dplyr::group_by_at(wdat.long, c("grupo","score.compreensao.quintile")),
score.compreensao ~ time,
p.adjust.method = "bonferroni")
lpwc[["grupo:score.compreensao.quintile"]] <- merge(plyr::rbind.fill(pwc, pwc.long),
lpwc[["grupo:score.compreensao.quintile"]],
by=c("grupo","score.compreensao.quintile","term",".y.","group1","group2"),
suffixes = c("","'"))
}
| Controle |
1st quintile |
time |
score.compreensao |
pre |
pos |
516 |
-2.054 |
0.041 |
0.041 |
* |
| Controle |
2nd quintile |
time |
score.compreensao |
pre |
pos |
516 |
-1.475 |
0.141 |
0.141 |
ns |
| Controle |
3rd quintile |
time |
score.compreensao |
pre |
pos |
516 |
2.254 |
0.025 |
0.025 |
* |
| Controle |
4th quintile |
time |
score.compreensao |
pre |
pos |
516 |
1.677 |
0.094 |
0.094 |
ns |
| Controle |
5th quintile |
time |
score.compreensao |
pre |
pos |
516 |
2.016 |
0.044 |
0.044 |
* |
| Experimental |
1st quintile |
time |
score.compreensao |
pre |
pos |
516 |
-3.857 |
0.000 |
0.000 |
*** |
| Experimental |
2nd quintile |
time |
score.compreensao |
pre |
pos |
516 |
-1.537 |
0.125 |
0.125 |
ns |
| Experimental |
3rd quintile |
time |
score.compreensao |
pre |
pos |
516 |
2.278 |
0.023 |
0.023 |
* |
| Experimental |
4th quintile |
time |
score.compreensao |
pre |
pos |
516 |
2.918 |
0.004 |
0.004 |
** |
| Experimental |
5th quintile |
time |
score.compreensao |
pre |
pos |
516 |
2.077 |
0.038 |
0.038 |
* |
if (length(unique(pdat[["score.compreensao.quintile"]])) >= 2) {
ds <- get.descriptives(wdat, "score.compreensao.pos", c("grupo","score.compreensao.quintile"), covar = "score.compreensao.pre")
ds <- merge(ds[ds$variable != "score.compreensao.pre",],
ds[ds$variable == "score.compreensao.pre", !colnames(ds) %in% c("variable")],
by = c("grupo","score.compreensao.quintile"), all.x = T, suffixes = c("", ".score.compreensao.pre"))
ds <- merge(get_emmeans(pwcs[["grupo"]]), ds,
by = c("grupo","score.compreensao.quintile"), suffixes = c(".emms", ""))
ds <- ds[,c("grupo","score.compreensao.quintile","n","mean.score.compreensao.pre","se.score.compreensao.pre",
"mean","se","emmean","se.emms","conf.low","conf.high")]
colnames(ds) <- c("grupo","score.compreensao.quintile", "N", paste0(c("M","SE")," (pre)"),
paste0(c("M","SE"), " (unadj)"),
paste0(c("M", "SE"), " (adj)"), "conf.low", "conf.high")
lemms[["grupo:score.compreensao.quintile"]] <- merge(ds, lemms[["grupo:score.compreensao.quintile"]],
by=c("grupo","score.compreensao.quintile"), suffixes = c("","'"))
}
| Controle |
1st quintile |
6 |
0.833 |
0.167 |
2.500 |
0.428 |
5.959 |
1.092 |
3.810 |
8.109 |
| Controle |
2nd quintile |
42 |
2.595 |
0.077 |
3.048 |
0.246 |
5.136 |
0.558 |
4.037 |
6.235 |
| Controle |
3rd quintile |
61 |
4.852 |
0.104 |
4.279 |
0.289 |
4.611 |
0.246 |
4.127 |
5.095 |
| Controle |
4th quintile |
26 |
7.462 |
0.100 |
6.808 |
0.392 |
5.110 |
0.530 |
4.066 |
6.155 |
| Controle |
5th quintile |
18 |
9.778 |
0.101 |
8.833 |
0.364 |
5.334 |
0.915 |
3.532 |
7.136 |
| Experimental |
1st quintile |
9 |
1.000 |
0.000 |
3.556 |
0.556 |
6.885 |
0.980 |
4.956 |
8.815 |
| Experimental |
2nd quintile |
21 |
2.286 |
0.101 |
2.952 |
0.375 |
5.282 |
0.669 |
3.965 |
6.599 |
| Experimental |
3rd quintile |
41 |
5.049 |
0.130 |
4.341 |
0.272 |
4.521 |
0.288 |
3.955 |
5.088 |
| Experimental |
4th quintile |
25 |
7.520 |
0.102 |
6.360 |
0.443 |
4.617 |
0.543 |
3.548 |
5.686 |
| Experimental |
5th quintile |
19 |
9.632 |
0.114 |
8.684 |
0.342 |
5.299 |
0.886 |
3.553 |
7.044 |
Plots for ancova
if (length(unique(pdat[["score.compreensao.quintile"]])) >= 2) {
ggPlotAoC2(pwcs, "grupo", "score.compreensao.quintile", aov, ylab = "Reading Comprehension",
subtitle = which(aov$Effect == "grupo:score.compreensao.quintile"), addParam = "errorbar") +
ggplot2::scale_color_manual(values = color[["score.compreensao.quintile"]]) +
ggplot2::ylab("Reading Comprehension") +
if (ymin.ci < ymax.ci) ggplot2::ylim(ymin.ci, ymax.ci)
}
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.

if (length(unique(pdat[["score.compreensao.quintile"]])) >= 2) {
ggPlotAoC2(pwcs, "score.compreensao.quintile", "grupo", aov, ylab = "Reading Comprehension",
subtitle = which(aov$Effect == "grupo:score.compreensao.quintile"), addParam = "errorbar") +
ggplot2::scale_color_manual(values = color[["grupo"]]) +
ggplot2::ylab("Reading Comprehension") +
if (ymin.ci < ymax.ci) ggplot2::ylim(ymin.ci, ymax.ci)
}
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.

if (length(unique(pdat[["score.compreensao.quintile"]])) >= 2) {
plots <- twoWayAncovaBoxPlots(
wdat, "score.compreensao.pos", c("grupo","score.compreensao.quintile"), aov, pwcs, covar = "score.compreensao.pre",
theme = "classic", color = color[["grupo:score.compreensao.quintile"]],
subtitle = which(aov$Effect == "grupo:score.compreensao.quintile"))
}
if (length(unique(pdat[["score.compreensao.quintile"]])) >= 2) {
plots[["grupo:score.compreensao.quintile"]] + ggplot2::ylab("Reading Comprehension") +
ggplot2::scale_x_discrete(labels=c('pre', 'pos')) +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}
## Warning: No shared levels found between `names(values)` of the manual scale and the data's colour values.

if (length(unique(pdat[["score.compreensao.quintile"]])) >= 2) {
plots <- twoWayAncovaBoxPlots(
wdat.long, "score.compreensao", c("grupo","score.compreensao.quintile"), aov, pwc.long,
pre.post = "time",
theme = "classic", color = color$prepost)
}
if (length(unique(pdat[["score.compreensao.quintile"]])) >= 2)
plots[["grupo:score.compreensao.quintile"]] + ggplot2::ylab("Reading Comprehension") +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)

Checking linearity
assumption
if (length(unique(pdat[["score.compreensao.quintile"]])) >= 2) {
ggscatter(wdat, x = "score.compreensao.pre", y = "score.compreensao.pos", size = 0.5,
facet.by = c("grupo","score.compreensao.quintile"), add = "reg.line")+
stat_regline_equation(
aes(label = paste(..eq.label.., ..rr.label.., sep = "~~~~"))
) + ggplot2::ylab("Reading Comprehension") +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}

if (length(unique(pdat[["score.compreensao.quintile"]])) >= 2) {
ggscatter(wdat, x = "score.compreensao.pre", y = "score.compreensao.pos", size = 0.5,
color = "grupo", facet.by = "score.compreensao.quintile", add = "reg.line")+
stat_regline_equation(
aes(label = paste(..eq.label.., ..rr.label.., sep = "~~~~"), color = grupo)
) +
ggplot2::labs(subtitle = rstatix::get_test_label(aov, detailed = T, row = which(aov$Effect == "grupo:score.compreensao.quintile"))) +
ggplot2::scale_color_manual(values = color[["grupo"]]) +
ggplot2::ylab("Reading Comprehension") +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}

if (length(unique(pdat[["score.compreensao.quintile"]])) >= 2) {
ggscatter(wdat, x = "score.compreensao.pre", y = "score.compreensao.pos", size = 0.5,
color = "score.compreensao.quintile", facet.by = "grupo", add = "reg.line")+
stat_regline_equation(
aes(label = paste(..eq.label.., ..rr.label.., sep = "~~~~"), color = score.compreensao.quintile)
) +
ggplot2::labs(subtitle = rstatix::get_test_label(aov, detailed = T, row = which(aov$Effect == "grupo:score.compreensao.quintile"))) +
ggplot2::scale_color_manual(values = color[["score.compreensao.quintile"]]) +
ggplot2::ylab("Reading Comprehension") +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}

Checking normality and
homogeneity
if (length(unique(pdat[["score.compreensao.quintile"]])) >= 2)
res <- augment(lm(score.compreensao.pos ~ score.compreensao.pre + grupo*score.compreensao.quintile, data = wdat))
if (length(unique(pdat[["score.compreensao.quintile"]])) >= 2)
shapiro_test(res$.resid)
## # A tibble: 1 × 3
## variable statistic p.value
## <chr> <dbl> <dbl>
## 1 res$.resid 0.993 0.196
if (length(unique(pdat[["score.compreensao.quintile"]])) >= 2)
levene_test(res, .resid ~ grupo*score.compreensao.quintile)
## # A tibble: 1 × 4
## df1 df2 statistic p
## <int> <int> <dbl> <dbl>
## 1 9 258 1.44 0.173
Summary of Results
Descriptive Statistics
df <- get.descriptives(ldat[["grupo"]], c(dv.pre, dv.pos), c("grupo"),
include.global = T, symmetry.test = T, normality.test = F)
df <- plyr::rbind.fill(
df, do.call(plyr::rbind.fill, lapply(lfatores2, FUN = function(f) {
if (nrow(dat) > 0 && sum(!is.na(unique(dat[[f]]))) > 1 && paste0("grupo:",f) %in% names(ldat))
get.descriptives(ldat[[paste0("grupo:",f)]], c(dv.pre,dv.pos), c("grupo", f),
symmetry.test = T, normality.test = F)
}))
)
df <- df[,c(fatores1[fatores1 %in% colnames(df)],"variable",
colnames(df)[!colnames(df) %in% c(fatores1,"variable")])]
| Controle |
|
|
|
|
score.compreensao.pre |
153 |
5.098 |
5.0 |
0 |
10 |
2.533 |
0.205 |
0.405 |
4.00 |
YES |
0.419 |
-0.742 |
| Experimental |
|
|
|
|
score.compreensao.pre |
115 |
5.522 |
6.0 |
1 |
10 |
2.773 |
0.259 |
0.512 |
5.00 |
YES |
-0.008 |
-1.121 |
|
|
|
|
|
score.compreensao.pre |
268 |
5.280 |
5.0 |
0 |
10 |
2.642 |
0.161 |
0.318 |
4.00 |
YES |
0.228 |
-0.953 |
| Controle |
|
|
|
|
score.compreensao.pos |
153 |
4.837 |
4.0 |
0 |
10 |
2.728 |
0.221 |
0.436 |
4.00 |
YES |
0.430 |
-0.917 |
| Experimental |
|
|
|
|
score.compreensao.pos |
115 |
5.183 |
5.0 |
0 |
10 |
2.624 |
0.245 |
0.485 |
4.00 |
YES |
0.305 |
-0.954 |
|
|
|
|
|
score.compreensao.pos |
268 |
4.985 |
5.0 |
0 |
10 |
2.684 |
0.164 |
0.323 |
4.00 |
YES |
0.372 |
-0.927 |
| Controle |
F |
|
|
|
score.compreensao.pre |
79 |
5.291 |
5.0 |
0 |
10 |
2.646 |
0.298 |
0.593 |
4.00 |
YES |
0.320 |
-0.902 |
| Controle |
M |
|
|
|
score.compreensao.pre |
74 |
4.892 |
4.0 |
1 |
10 |
2.408 |
0.280 |
0.558 |
3.00 |
NO |
0.503 |
-0.613 |
| Experimental |
F |
|
|
|
score.compreensao.pre |
55 |
6.345 |
6.0 |
1 |
10 |
2.654 |
0.358 |
0.718 |
3.00 |
YES |
-0.245 |
-0.919 |
| Experimental |
M |
|
|
|
score.compreensao.pre |
60 |
4.767 |
4.0 |
1 |
10 |
2.683 |
0.346 |
0.693 |
5.00 |
YES |
0.214 |
-1.189 |
| Controle |
F |
|
|
|
score.compreensao.pos |
79 |
5.253 |
5.0 |
1 |
10 |
2.574 |
0.290 |
0.577 |
4.00 |
YES |
0.359 |
-0.866 |
| Controle |
M |
|
|
|
score.compreensao.pos |
74 |
4.392 |
4.0 |
0 |
10 |
2.832 |
0.329 |
0.656 |
5.00 |
NO |
0.586 |
-0.912 |
| Experimental |
F |
|
|
|
score.compreensao.pos |
55 |
5.964 |
6.0 |
1 |
10 |
2.742 |
0.370 |
0.741 |
4.50 |
YES |
-0.079 |
-1.137 |
| Experimental |
M |
|
|
|
score.compreensao.pos |
60 |
4.467 |
4.0 |
0 |
10 |
2.310 |
0.298 |
0.597 |
3.00 |
NO |
0.601 |
-0.406 |
| Controle |
|
Rural |
|
|
score.compreensao.pre |
51 |
4.608 |
4.0 |
1 |
10 |
2.359 |
0.330 |
0.663 |
3.00 |
NO |
0.813 |
-0.092 |
| Controle |
|
Urbana |
|
|
score.compreensao.pre |
67 |
5.194 |
5.0 |
1 |
10 |
2.439 |
0.298 |
0.595 |
4.00 |
YES |
0.321 |
-0.815 |
| Experimental |
|
Rural |
|
|
score.compreensao.pre |
46 |
5.457 |
5.5 |
1 |
10 |
2.730 |
0.403 |
0.811 |
4.50 |
YES |
-0.037 |
-1.146 |
| Experimental |
|
Urbana |
|
|
score.compreensao.pre |
43 |
5.256 |
6.0 |
1 |
10 |
2.821 |
0.430 |
0.868 |
5.50 |
YES |
-0.069 |
-1.251 |
| Controle |
|
Rural |
|
|
score.compreensao.pos |
51 |
4.353 |
4.0 |
1 |
10 |
2.644 |
0.370 |
0.744 |
3.00 |
NO |
0.890 |
-0.151 |
| Controle |
|
Urbana |
|
|
score.compreensao.pos |
67 |
5.194 |
5.0 |
1 |
10 |
2.693 |
0.329 |
0.657 |
4.00 |
YES |
0.205 |
-1.062 |
| Experimental |
|
Rural |
|
|
score.compreensao.pos |
46 |
5.217 |
5.0 |
0 |
10 |
2.724 |
0.402 |
0.809 |
4.75 |
YES |
0.163 |
-1.146 |
| Experimental |
|
Urbana |
|
|
score.compreensao.pos |
43 |
5.326 |
5.0 |
1 |
10 |
2.598 |
0.396 |
0.799 |
4.00 |
YES |
0.294 |
-0.966 |
| Controle |
|
|
Rural |
|
score.compreensao.pre |
52 |
5.654 |
5.0 |
1 |
10 |
2.351 |
0.326 |
0.654 |
4.00 |
YES |
0.392 |
-0.947 |
| Controle |
|
|
Urbana |
|
score.compreensao.pre |
101 |
4.812 |
4.0 |
0 |
10 |
2.587 |
0.257 |
0.511 |
4.00 |
NO |
0.500 |
-0.674 |
| Experimental |
|
|
Rural |
|
score.compreensao.pre |
35 |
6.229 |
6.0 |
1 |
10 |
2.734 |
0.462 |
0.939 |
4.00 |
YES |
-0.158 |
-1.011 |
| Experimental |
|
|
Urbana |
|
score.compreensao.pre |
80 |
5.213 |
5.0 |
1 |
10 |
2.750 |
0.307 |
0.612 |
4.50 |
YES |
0.059 |
-1.204 |
| Controle |
|
|
Rural |
|
score.compreensao.pos |
52 |
5.173 |
4.5 |
1 |
10 |
2.728 |
0.378 |
0.759 |
4.25 |
YES |
0.362 |
-1.128 |
| Controle |
|
|
Urbana |
|
score.compreensao.pos |
101 |
4.663 |
4.0 |
0 |
10 |
2.725 |
0.271 |
0.538 |
5.00 |
YES |
0.467 |
-0.844 |
| Experimental |
|
|
Rural |
|
score.compreensao.pos |
35 |
5.486 |
5.0 |
1 |
10 |
2.853 |
0.482 |
0.980 |
4.50 |
YES |
0.147 |
-1.269 |
| Experimental |
|
|
Urbana |
|
score.compreensao.pos |
80 |
5.050 |
5.0 |
0 |
10 |
2.525 |
0.282 |
0.562 |
4.00 |
YES |
0.359 |
-0.836 |
| Controle |
|
|
|
1st quintile |
score.compreensao.pre |
6 |
0.833 |
1.0 |
0 |
1 |
0.408 |
0.167 |
0.428 |
0.00 |
few data |
0.000 |
0.000 |
| Controle |
|
|
|
2nd quintile |
score.compreensao.pre |
42 |
2.595 |
3.0 |
2 |
3 |
0.497 |
0.077 |
0.155 |
1.00 |
few data |
0.000 |
0.000 |
| Controle |
|
|
|
3rd quintile |
score.compreensao.pre |
61 |
4.852 |
5.0 |
4 |
6 |
0.813 |
0.104 |
0.208 |
2.00 |
few data |
0.000 |
0.000 |
| Controle |
|
|
|
4th quintile |
score.compreensao.pre |
26 |
7.462 |
7.0 |
7 |
8 |
0.508 |
0.100 |
0.205 |
1.00 |
few data |
0.000 |
0.000 |
| Controle |
|
|
|
5th quintile |
score.compreensao.pre |
18 |
9.778 |
10.0 |
9 |
10 |
0.428 |
0.101 |
0.213 |
0.00 |
few data |
0.000 |
0.000 |
| Experimental |
|
|
|
1st quintile |
score.compreensao.pre |
9 |
1.000 |
1.0 |
1 |
1 |
0.000 |
0.000 |
0.000 |
0.00 |
few data |
0.000 |
0.000 |
| Experimental |
|
|
|
2nd quintile |
score.compreensao.pre |
21 |
2.286 |
2.0 |
2 |
3 |
0.463 |
0.101 |
0.211 |
1.00 |
few data |
0.000 |
0.000 |
| Experimental |
|
|
|
3rd quintile |
score.compreensao.pre |
41 |
5.049 |
5.0 |
4 |
6 |
0.835 |
0.130 |
0.264 |
2.00 |
few data |
0.000 |
0.000 |
| Experimental |
|
|
|
4th quintile |
score.compreensao.pre |
25 |
7.520 |
8.0 |
7 |
8 |
0.510 |
0.102 |
0.210 |
1.00 |
few data |
0.000 |
0.000 |
| Experimental |
|
|
|
5th quintile |
score.compreensao.pre |
19 |
9.632 |
10.0 |
9 |
10 |
0.496 |
0.114 |
0.239 |
1.00 |
few data |
0.000 |
0.000 |
| Controle |
|
|
|
1st quintile |
score.compreensao.pos |
6 |
2.500 |
2.5 |
1 |
4 |
1.049 |
0.428 |
1.101 |
1.00 |
YES |
0.000 |
-1.571 |
| Controle |
|
|
|
2nd quintile |
score.compreensao.pos |
42 |
3.048 |
3.0 |
0 |
6 |
1.592 |
0.246 |
0.496 |
2.00 |
YES |
0.207 |
-0.865 |
| Controle |
|
|
|
3rd quintile |
score.compreensao.pos |
61 |
4.279 |
4.0 |
1 |
10 |
2.259 |
0.289 |
0.579 |
3.00 |
NO |
0.690 |
-0.222 |
| Controle |
|
|
|
4th quintile |
score.compreensao.pos |
26 |
6.808 |
7.0 |
2 |
10 |
2.000 |
0.392 |
0.808 |
2.00 |
NO |
-0.553 |
-0.545 |
| Controle |
|
|
|
5th quintile |
score.compreensao.pos |
18 |
8.833 |
10.0 |
5 |
10 |
1.543 |
0.364 |
0.768 |
2.00 |
NO |
-0.917 |
-0.359 |
| Experimental |
|
|
|
1st quintile |
score.compreensao.pos |
9 |
3.556 |
3.0 |
2 |
6 |
1.667 |
0.556 |
1.281 |
3.00 |
YES |
0.490 |
-1.652 |
| Experimental |
|
|
|
2nd quintile |
score.compreensao.pos |
21 |
2.952 |
3.0 |
0 |
7 |
1.717 |
0.375 |
0.782 |
2.00 |
NO |
0.522 |
-0.256 |
| Experimental |
|
|
|
3rd quintile |
score.compreensao.pos |
41 |
4.341 |
4.0 |
1 |
8 |
1.741 |
0.272 |
0.549 |
2.00 |
YES |
0.260 |
-0.738 |
| Experimental |
|
|
|
4th quintile |
score.compreensao.pos |
25 |
6.360 |
7.0 |
2 |
10 |
2.215 |
0.443 |
0.914 |
3.00 |
YES |
-0.086 |
-1.022 |
| Experimental |
|
|
|
5th quintile |
score.compreensao.pos |
19 |
8.684 |
9.0 |
5 |
10 |
1.493 |
0.342 |
0.719 |
2.00 |
NO |
-1.003 |
-0.098 |
ANCOVA Table Comparison
df <- do.call(plyr::rbind.fill, laov)
df <- df[!duplicated(df$Effect),]
| 1 |
grupo |
1 |
265 |
0.023 |
0.880 |
|
0.000 |
1 |
265 |
0.023 |
0.880 |
|
0.000 |
| 2 |
score.compreensao.pre |
1 |
265 |
288.858 |
0.000 |
* |
0.522 |
1 |
265 |
288.858 |
0.000 |
* |
0.522 |
| 3 |
genero |
1 |
263 |
4.429 |
0.036 |
* |
0.017 |
1 |
263 |
4.429 |
0.036 |
* |
0.017 |
| 5 |
grupo:genero |
1 |
263 |
0.218 |
0.641 |
|
0.001 |
1 |
263 |
0.218 |
0.641 |
|
0.001 |
| 8 |
grupo:zona.participante |
1 |
202 |
0.081 |
0.777 |
|
0.000 |
1 |
202 |
0.081 |
0.777 |
|
0.000 |
| 10 |
zona.participante |
1 |
202 |
1.692 |
0.195 |
|
0.008 |
1 |
202 |
1.692 |
0.195 |
|
0.008 |
| 12 |
grupo:zona.escola |
1 |
263 |
0.169 |
0.682 |
|
0.001 |
1 |
263 |
0.169 |
0.682 |
|
0.001 |
| 14 |
zona.escola |
1 |
263 |
0.643 |
0.423 |
|
0.002 |
1 |
263 |
0.643 |
0.423 |
|
0.002 |
| 16 |
grupo:score.compreensao.quintile |
4 |
257 |
0.489 |
0.744 |
|
0.008 |
4 |
257 |
0.489 |
0.744 |
|
0.008 |
| 18 |
score.compreensao.quintile |
4 |
257 |
4.226 |
0.002 |
* |
0.062 |
4 |
257 |
4.226 |
0.002 |
* |
0.062 |
PairWise Table Comparison
df <- do.call(plyr::rbind.fill, lpwc)
df <- df[,c(names(lfatores)[names(lfatores) %in% colnames(df)],
names(df)[!names(df) %in% c(names(lfatores),"term",".y.")])]
| Controle |
|
|
|
|
pre |
pos |
532 |
0.859 |
0.391 |
0.391 |
ns |
532 |
0.859 |
0.391 |
0.391 |
ns |
| Experimental |
|
|
|
|
pre |
pos |
532 |
0.966 |
0.334 |
0.334 |
ns |
532 |
0.966 |
0.334 |
0.334 |
ns |
|
|
|
|
|
Controle |
Experimental |
265 |
-0.151 |
0.880 |
0.880 |
ns |
265 |
-0.151 |
0.880 |
0.880 |
ns |
| Controle |
F |
|
|
|
pre |
pos |
528 |
0.091 |
0.927 |
0.927 |
ns |
528 |
0.091 |
0.927 |
0.927 |
ns |
| Controle |
M |
|
|
|
pre |
pos |
528 |
1.165 |
0.245 |
0.245 |
ns |
528 |
1.165 |
0.245 |
0.245 |
ns |
| Controle |
|
|
|
|
F |
M |
263 |
1.912 |
0.057 |
0.057 |
ns |
263 |
1.912 |
0.057 |
0.057 |
ns |
| Experimental |
F |
|
|
|
pre |
pos |
528 |
0.767 |
0.443 |
0.443 |
ns |
528 |
0.767 |
0.443 |
0.443 |
ns |
| Experimental |
M |
|
|
|
pre |
pos |
528 |
0.629 |
0.529 |
0.529 |
ns |
528 |
0.629 |
0.529 |
0.529 |
ns |
| Experimental |
|
|
|
|
F |
M |
263 |
1.018 |
0.310 |
0.310 |
ns |
263 |
1.018 |
0.310 |
0.310 |
ns |
|
F |
|
|
|
Controle |
Experimental |
263 |
0.151 |
0.880 |
0.880 |
ns |
263 |
0.151 |
0.880 |
0.880 |
ns |
|
M |
|
|
|
Controle |
Experimental |
263 |
-0.513 |
0.608 |
0.608 |
ns |
263 |
-0.513 |
0.608 |
0.608 |
ns |
| Controle |
|
|
|
|
Rural |
Urbana |
202 |
-1.164 |
0.246 |
0.246 |
ns |
202 |
-1.164 |
0.246 |
0.246 |
ns |
| Controle |
|
Rural |
|
|
pre |
pos |
406 |
0.491 |
0.623 |
0.623 |
ns |
406 |
0.491 |
0.623 |
0.623 |
ns |
| Controle |
|
Urbana |
|
|
pre |
pos |
406 |
0.000 |
1.000 |
1.000 |
ns |
406 |
0.000 |
1.000 |
1.000 |
ns |
| Experimental |
|
|
|
|
Rural |
Urbana |
202 |
-0.644 |
0.520 |
0.520 |
ns |
202 |
-0.644 |
0.520 |
0.520 |
ns |
| Experimental |
|
Rural |
|
|
pre |
pos |
406 |
0.438 |
0.662 |
0.662 |
ns |
406 |
0.438 |
0.662 |
0.662 |
ns |
| Experimental |
|
Urbana |
|
|
pre |
pos |
406 |
-0.124 |
0.902 |
0.902 |
ns |
406 |
-0.124 |
0.902 |
0.902 |
ns |
|
|
Rural |
|
|
Controle |
Experimental |
202 |
-0.616 |
0.539 |
0.539 |
ns |
202 |
-0.616 |
0.539 |
0.539 |
ns |
|
|
Urbana |
|
|
Controle |
Experimental |
202 |
-0.234 |
0.815 |
0.815 |
ns |
202 |
-0.234 |
0.815 |
0.815 |
ns |
| Controle |
|
|
|
|
Rural |
Urbana |
263 |
-0.355 |
0.723 |
0.723 |
ns |
263 |
-0.355 |
0.723 |
0.723 |
ns |
| Controle |
|
|
Rural |
|
pre |
pos |
528 |
0.925 |
0.355 |
0.355 |
ns |
528 |
0.925 |
0.355 |
0.355 |
ns |
| Controle |
|
|
Urbana |
|
pre |
pos |
528 |
0.398 |
0.690 |
0.690 |
ns |
528 |
0.398 |
0.690 |
0.690 |
ns |
| Experimental |
|
|
|
|
Rural |
Urbana |
263 |
-0.833 |
0.406 |
0.406 |
ns |
263 |
-0.833 |
0.406 |
0.406 |
ns |
| Experimental |
|
|
Rural |
|
pre |
pos |
528 |
1.173 |
0.241 |
0.241 |
ns |
528 |
1.173 |
0.241 |
0.241 |
ns |
| Experimental |
|
|
Urbana |
|
pre |
pos |
528 |
0.388 |
0.698 |
0.698 |
ns |
528 |
0.388 |
0.698 |
0.698 |
ns |
|
|
|
Rural |
|
Controle |
Experimental |
263 |
0.277 |
0.782 |
0.782 |
ns |
263 |
0.277 |
0.782 |
0.782 |
ns |
|
|
|
Urbana |
|
Controle |
Experimental |
263 |
-0.322 |
0.748 |
0.748 |
ns |
263 |
-0.322 |
0.748 |
0.748 |
ns |
| Controle |
|
|
|
1st quintile |
pre |
pos |
516 |
-2.054 |
0.041 |
0.041 |
* |
516 |
-2.054 |
0.041 |
0.041 |
* |
| Controle |
|
|
|
2nd quintile |
pre |
pos |
516 |
-1.475 |
0.141 |
0.141 |
ns |
516 |
-1.475 |
0.141 |
0.141 |
ns |
| Controle |
|
|
|
3rd quintile |
pre |
pos |
516 |
2.254 |
0.025 |
0.025 |
* |
516 |
2.254 |
0.025 |
0.025 |
* |
| Controle |
|
|
|
4th quintile |
pre |
pos |
516 |
1.677 |
0.094 |
0.094 |
ns |
516 |
1.677 |
0.094 |
0.094 |
ns |
| Controle |
|
|
|
5th quintile |
pre |
pos |
516 |
2.016 |
0.044 |
0.044 |
* |
516 |
2.016 |
0.044 |
0.044 |
* |
| Controle |
|
|
|
|
1st quintile |
2nd quintile |
257 |
0.961 |
0.337 |
1.000 |
ns |
257 |
0.961 |
0.337 |
1.000 |
ns |
| Controle |
|
|
|
|
1st quintile |
3rd quintile |
257 |
1.269 |
0.206 |
1.000 |
ns |
257 |
1.269 |
0.206 |
1.000 |
ns |
| Controle |
|
|
|
|
1st quintile |
4th quintile |
257 |
0.586 |
0.558 |
1.000 |
ns |
257 |
0.586 |
0.558 |
1.000 |
ns |
| Controle |
|
|
|
|
1st quintile |
5th quintile |
257 |
0.343 |
0.732 |
1.000 |
ns |
257 |
0.343 |
0.732 |
1.000 |
ns |
| Controle |
|
|
|
|
2nd quintile |
3rd quintile |
257 |
0.962 |
0.337 |
1.000 |
ns |
257 |
0.962 |
0.337 |
1.000 |
ns |
| Controle |
|
|
|
|
2nd quintile |
4th quintile |
257 |
0.026 |
0.979 |
1.000 |
ns |
257 |
0.026 |
0.979 |
1.000 |
ns |
| Controle |
|
|
|
|
2nd quintile |
5th quintile |
257 |
-0.142 |
0.887 |
1.000 |
ns |
257 |
-0.142 |
0.887 |
1.000 |
ns |
| Controle |
|
|
|
|
3rd quintile |
4th quintile |
257 |
-0.787 |
0.432 |
1.000 |
ns |
257 |
-0.787 |
0.432 |
1.000 |
ns |
| Controle |
|
|
|
|
3rd quintile |
5th quintile |
257 |
-0.715 |
0.475 |
1.000 |
ns |
257 |
-0.715 |
0.475 |
1.000 |
ns |
| Controle |
|
|
|
|
4th quintile |
5th quintile |
257 |
-0.321 |
0.749 |
1.000 |
ns |
257 |
-0.321 |
0.749 |
1.000 |
ns |
| Experimental |
|
|
|
1st quintile |
pre |
pos |
516 |
-3.857 |
0.000 |
0.000 |
*** |
516 |
-3.857 |
0.000 |
0.000 |
*** |
| Experimental |
|
|
|
2nd quintile |
pre |
pos |
516 |
-1.537 |
0.125 |
0.125 |
ns |
516 |
-1.537 |
0.125 |
0.125 |
ns |
| Experimental |
|
|
|
3rd quintile |
pre |
pos |
516 |
2.278 |
0.023 |
0.023 |
* |
516 |
2.278 |
0.023 |
0.023 |
* |
| Experimental |
|
|
|
4th quintile |
pre |
pos |
516 |
2.918 |
0.004 |
0.004 |
** |
516 |
2.918 |
0.004 |
0.004 |
** |
| Experimental |
|
|
|
5th quintile |
pre |
pos |
516 |
2.077 |
0.038 |
0.038 |
* |
516 |
2.077 |
0.038 |
0.038 |
* |
| Experimental |
|
|
|
|
1st quintile |
2nd quintile |
257 |
2.104 |
0.036 |
0.364 |
ns |
257 |
2.104 |
0.036 |
0.364 |
ns |
| Experimental |
|
|
|
|
1st quintile |
3rd quintile |
257 |
2.389 |
0.018 |
0.176 |
ns |
257 |
2.389 |
0.018 |
0.176 |
ns |
| Experimental |
|
|
|
|
1st quintile |
4th quintile |
257 |
1.657 |
0.099 |
0.987 |
ns |
257 |
1.657 |
0.099 |
0.987 |
ns |
| Experimental |
|
|
|
|
1st quintile |
5th quintile |
257 |
0.924 |
0.356 |
1.000 |
ns |
257 |
0.924 |
0.356 |
1.000 |
ns |
| Experimental |
|
|
|
|
2nd quintile |
3rd quintile |
257 |
1.092 |
0.276 |
1.000 |
ns |
257 |
1.092 |
0.276 |
1.000 |
ns |
| Experimental |
|
|
|
|
2nd quintile |
4th quintile |
257 |
0.613 |
0.540 |
1.000 |
ns |
257 |
0.613 |
0.540 |
1.000 |
ns |
| Experimental |
|
|
|
|
2nd quintile |
5th quintile |
257 |
-0.012 |
0.991 |
1.000 |
ns |
257 |
-0.012 |
0.991 |
1.000 |
ns |
| Experimental |
|
|
|
|
3rd quintile |
4th quintile |
257 |
-0.150 |
0.881 |
1.000 |
ns |
257 |
-0.150 |
0.881 |
1.000 |
ns |
| Experimental |
|
|
|
|
3rd quintile |
5th quintile |
257 |
-0.805 |
0.422 |
1.000 |
ns |
257 |
-0.805 |
0.422 |
1.000 |
ns |
| Experimental |
|
|
|
|
4th quintile |
5th quintile |
257 |
-1.014 |
0.312 |
1.000 |
ns |
257 |
-1.014 |
0.312 |
1.000 |
ns |
|
|
|
|
1st quintile |
Controle |
Experimental |
257 |
-0.963 |
0.336 |
0.336 |
ns |
257 |
-0.963 |
0.336 |
0.336 |
ns |
|
|
|
|
2nd quintile |
Controle |
Experimental |
257 |
-0.297 |
0.767 |
0.767 |
ns |
257 |
-0.297 |
0.767 |
0.767 |
ns |
|
|
|
|
3rd quintile |
Controle |
Experimental |
257 |
0.243 |
0.808 |
0.808 |
ns |
257 |
0.243 |
0.808 |
0.808 |
ns |
|
|
|
|
4th quintile |
Controle |
Experimental |
257 |
0.966 |
0.335 |
0.335 |
ns |
257 |
0.966 |
0.335 |
0.335 |
ns |
|
|
|
|
5th quintile |
Controle |
Experimental |
257 |
0.059 |
0.953 |
0.953 |
ns |
257 |
0.059 |
0.953 |
0.953 |
ns |
EMMS Table Comparison
df <- do.call(plyr::rbind.fill, lemms)
df[["N-N'"]] <- df[["N"]] - df[["N'"]]
df <- df[,c(names(lfatores)[names(lfatores) %in% colnames(df)],
names(df)[!names(df) %in% names(lfatores)])]
| Controle |
|
|
|
|
153 |
5.098 |
0.205 |
4.837 |
0.221 |
4.970 |
0.151 |
4.674 |
5.267 |
153 |
5.098 |
0.205 |
4.837 |
0.221 |
4.970 |
0.151 |
4.674 |
5.267 |
0 |
| Experimental |
|
|
|
|
115 |
5.522 |
0.259 |
5.183 |
0.245 |
5.005 |
0.174 |
4.663 |
5.347 |
115 |
5.522 |
0.259 |
5.183 |
0.245 |
5.005 |
0.174 |
4.663 |
5.347 |
0 |
| Controle |
F |
|
|
|
79 |
5.291 |
0.298 |
5.253 |
0.290 |
5.245 |
0.208 |
4.835 |
5.655 |
79 |
5.291 |
0.298 |
5.253 |
0.290 |
5.245 |
0.208 |
4.835 |
5.655 |
0 |
| Controle |
M |
|
|
|
74 |
4.892 |
0.280 |
4.392 |
0.329 |
4.672 |
0.216 |
4.247 |
5.097 |
74 |
4.892 |
0.280 |
4.392 |
0.329 |
4.672 |
0.216 |
4.247 |
5.097 |
0 |
| Experimental |
F |
|
|
|
55 |
6.345 |
0.358 |
5.964 |
0.370 |
5.195 |
0.254 |
4.695 |
5.695 |
55 |
6.345 |
0.358 |
5.964 |
0.370 |
5.195 |
0.254 |
4.695 |
5.695 |
0 |
| Experimental |
M |
|
|
|
60 |
4.767 |
0.346 |
4.467 |
0.298 |
4.837 |
0.240 |
4.364 |
5.309 |
60 |
4.767 |
0.346 |
4.467 |
0.298 |
4.837 |
0.240 |
4.364 |
5.309 |
0 |
| Controle |
|
Rural |
|
|
51 |
4.608 |
0.330 |
4.353 |
0.370 |
4.732 |
0.264 |
4.211 |
5.253 |
51 |
4.608 |
0.330 |
4.353 |
0.370 |
4.732 |
0.264 |
4.211 |
5.253 |
0 |
| Controle |
|
Urbana |
|
|
67 |
5.194 |
0.298 |
5.194 |
0.329 |
5.140 |
0.229 |
4.688 |
5.592 |
67 |
5.194 |
0.298 |
5.194 |
0.329 |
5.140 |
0.229 |
4.688 |
5.592 |
0 |
| Experimental |
|
Rural |
|
|
46 |
5.457 |
0.403 |
5.217 |
0.402 |
4.969 |
0.277 |
4.422 |
5.516 |
46 |
5.457 |
0.403 |
5.217 |
0.402 |
4.969 |
0.277 |
4.422 |
5.516 |
0 |
| Experimental |
|
Urbana |
|
|
43 |
5.256 |
0.430 |
5.326 |
0.396 |
5.226 |
0.286 |
4.661 |
5.790 |
43 |
5.256 |
0.430 |
5.326 |
0.396 |
5.226 |
0.286 |
4.661 |
5.790 |
0 |
| Controle |
|
|
Rural |
|
52 |
5.654 |
0.326 |
5.173 |
0.378 |
4.896 |
0.259 |
4.386 |
5.406 |
52 |
5.654 |
0.326 |
5.173 |
0.378 |
4.896 |
0.259 |
4.386 |
5.406 |
0 |
| Controle |
|
|
Urbana |
|
101 |
4.812 |
0.257 |
4.663 |
0.271 |
5.010 |
0.187 |
4.642 |
5.377 |
101 |
4.812 |
0.257 |
4.663 |
0.271 |
5.010 |
0.187 |
4.642 |
5.377 |
0 |
| Experimental |
|
|
Rural |
|
35 |
6.229 |
0.462 |
5.486 |
0.482 |
4.783 |
0.318 |
4.157 |
5.409 |
35 |
6.229 |
0.462 |
5.486 |
0.482 |
4.783 |
0.318 |
4.157 |
5.409 |
0 |
| Experimental |
|
|
Urbana |
|
80 |
5.213 |
0.307 |
5.050 |
0.282 |
5.100 |
0.208 |
4.689 |
5.510 |
80 |
5.213 |
0.307 |
5.050 |
0.282 |
5.100 |
0.208 |
4.689 |
5.510 |
0 |
| Controle |
|
|
|
1st quintile |
6 |
0.833 |
0.167 |
2.500 |
0.428 |
5.959 |
1.092 |
3.810 |
8.109 |
6 |
0.833 |
0.167 |
2.500 |
0.428 |
5.959 |
1.092 |
3.810 |
8.109 |
0 |
| Controle |
|
|
|
2nd quintile |
42 |
2.595 |
0.077 |
3.048 |
0.246 |
5.136 |
0.558 |
4.037 |
6.235 |
42 |
2.595 |
0.077 |
3.048 |
0.246 |
5.136 |
0.558 |
4.037 |
6.235 |
0 |
| Controle |
|
|
|
3rd quintile |
61 |
4.852 |
0.104 |
4.279 |
0.289 |
4.611 |
0.246 |
4.127 |
5.095 |
61 |
4.852 |
0.104 |
4.279 |
0.289 |
4.611 |
0.246 |
4.127 |
5.095 |
0 |
| Controle |
|
|
|
4th quintile |
26 |
7.462 |
0.100 |
6.808 |
0.392 |
5.110 |
0.530 |
4.066 |
6.155 |
26 |
7.462 |
0.100 |
6.808 |
0.392 |
5.110 |
0.530 |
4.066 |
6.155 |
0 |
| Controle |
|
|
|
5th quintile |
18 |
9.778 |
0.101 |
8.833 |
0.364 |
5.334 |
0.915 |
3.532 |
7.136 |
18 |
9.778 |
0.101 |
8.833 |
0.364 |
5.334 |
0.915 |
3.532 |
7.136 |
0 |
| Experimental |
|
|
|
1st quintile |
9 |
1.000 |
0.000 |
3.556 |
0.556 |
6.885 |
0.980 |
4.956 |
8.815 |
9 |
1.000 |
0.000 |
3.556 |
0.556 |
6.885 |
0.980 |
4.956 |
8.815 |
0 |
| Experimental |
|
|
|
2nd quintile |
21 |
2.286 |
0.101 |
2.952 |
0.375 |
5.282 |
0.669 |
3.965 |
6.599 |
21 |
2.286 |
0.101 |
2.952 |
0.375 |
5.282 |
0.669 |
3.965 |
6.599 |
0 |
| Experimental |
|
|
|
3rd quintile |
41 |
5.049 |
0.130 |
4.341 |
0.272 |
4.521 |
0.288 |
3.955 |
5.088 |
41 |
5.049 |
0.130 |
4.341 |
0.272 |
4.521 |
0.288 |
3.955 |
5.088 |
0 |
| Experimental |
|
|
|
4th quintile |
25 |
7.520 |
0.102 |
6.360 |
0.443 |
4.617 |
0.543 |
3.548 |
5.686 |
25 |
7.520 |
0.102 |
6.360 |
0.443 |
4.617 |
0.543 |
3.548 |
5.686 |
0 |
| Experimental |
|
|
|
5th quintile |
19 |
9.632 |
0.114 |
8.684 |
0.342 |
5.299 |
0.886 |
3.553 |
7.044 |
19 |
9.632 |
0.114 |
8.684 |
0.342 |
5.299 |
0.886 |
3.553 |
7.044 |
0 |